Microgrids: Design, Challenges, and Prospects (Big Data for Industry 4.0) [1 ed.] 0367487950, 9780367487959

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Microgrids

Big Data for Industry 4.0: Challenges and Applications Series Editors: Sandhya Makkar, K. Martin Sagayam, and Rohail Hassan Industry 4.0, or the fourth industrial revolution, refers to interconnectivity, automation and real-time data exchange between machines and processes. There is tremendous growth in big data from the Internet of Things (IoT) and information services which drives the industry to develop new models and distributed tools to handle big data. Cutting-edge digital technologies are being harnessed to optimize and automate production, including upstream supply-chain processes, warehouse management systems, automated guided vehicles, drones and so on. The ultimate goal of Industry 4.0 is to drive manufacturing or services in a progressive way to be faster, effective and efficient, which can only be achieved by embedding modern-day technology in machines, components and parts that will transmit real-time data to networked IT systems. These, in turn, apply advanced soft computing paradigms such as machine learning algorithms to run the process automatically without any manual operations. The new book series will provide readers with an overview of the state-of-the-art in the field of Industry 4.0 and related research advancements. The books will identify and discuss new dimensions of both risk factors and success factors, along with performance metrics that can be employed in future research work. The series will also discuss a number of real-time issues, problems and applications with corresponding solutions and suggestions, sharing new theoretical findings, tools and techniques for Industry 4.0 and covering both theoretical and application-oriented approaches. The book series will offer a valuable asset for newcomers to the field and practicing professionals alike. The focus is to collate the recent advances in the field so that undergraduate and postgraduate students, researchers, academicians and industry people can easily understand the implications and applications of the field. Industry 4.0 Interoperability, Analytics, Security, and Case Studies Edited by G. Rajesh, X. Mercilin Raajini, and Hien Dang Big Data and Artificial Intelligence for Healthcare Applications Edited by Ankur Saxena, Nicolas Brault, and Shazia Rashid Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems Edited by K. Suganthi, R. Karthik, G. Rajesh, and Ho Chiung Ching Big Data for Entrepreneurship and Sustainable Development Edited by Mohammed el Amine Abdelli, Wissem Ajili Ben Youssef, Ugur Ozgoker, and Imen Ben Slimene Entrepreneurship and Big Data The Digital Revolution Edited by Meghna Chhabra, Rohail Hassan, and Amjad Shamim Microgrids Design, Challenges, and Prospects Edited by Ghous Bakhsh Narejo, Biswaranjan Acharya, Ranjit Singh Sarban Singh, and Fatma Newagy For more information on this series, please visit: www.routledge.com/Big-Data-for-Industry4.0-Challenges-and-Applications/book-series/CRCBDICA

Microgrids Design, Challenges, and Prospects

Edited by Ghous Bakhsh Narejo, Biswaranjan Acharya, Ranjit Singh Sarban Singh, and Fatma Newagy

MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software. First edition published 2022 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487–2742 and by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2022 Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www. copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978–750–8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data Names: Narejo, Ghous Bakhsh, editor. Title: Microgrids : design, challenges, and prospects / edited by Ghous Bakhsh Narejo, Biswaranjan Acharya, Ranjit Singh Sarban Singh, and Fatma Newagy. Other titles: Microgrids (CRC Press) Description: First edition. | Boca Raton : CRC Press, 2022. | Series: Big data for industry 4.0 : challenges and applications | Includes bibliographical references and index. Identifiers: LCCN 2021018047 (print) | LCCN 2021018048 (ebook) | ISBN 9780367487959 (hbk) | ISBN 9780367639846 (pbk) | ISBN 9781003121626 (ebk) Subjects: LCSH: Microgrids (Smart power grids) Classification: LCC TK3105 .M5524 2022 (print) | LCC TK3105 (ebook) | DDC 621.31—dc23 LC record available at https://lccn.loc.gov/2021018047 LC ebook record available at https://lccn.loc.gov/2021018048 ISBN: 978-0-367-48795-9 (hbk) ISBN: 978-0-367-63984-6 (pbk) ISBN: 978-1-003-12162-6 (ebk) DOI: 10.1201/9781003121626 Typeset in Times by Apex CoVantage, LLC

Contents Preface��������������������������������������������������������������������������������������������������������������������� vii Editors����������������������������������������������������������������������������������������������������������������������� ix Chapter 1 Microgrid Design and Challenges for Remote Communities�������������� 1 Fawad Azeem, Ghous Bakhsh Narejo, Waleed Rafique, Aizaz Mohiuddin, and Tauseef Anwar Chapter 2 Microgrid Design Evolution and Architecture������������������������������������19 Kumari Namrata, Ch Sekhar, D.P. Kothari, and Sriparna Das Chapter 3 Renewable Energy Source Design in Microgrids�������������������������������53 Kumari Namrata, Sriparna Das, R.P. Saini, and Ch Sekhar Chapter 4 Microgrid Power System Control Designs����������������������������������������� 83 Renuka Loka, Alivelu M. Parimi, and P. Shambhu Prasad Chapter 5 Hybrid Microgrid Design Based on Environment, Reliability, and Economic Aspects����������������������������������������������������������������������101 Sriparna Roy Ghatak, Aashish Kumar Bohre, and Parimal Acharjee Chapter 6 Trends in Microgrid Control�������������������������������������������������������������119 Anup Kumar Nanda, Babita Panda, Chinmoy Kumar Panigrahi, Arjyadhara Pradhan, and Naeem Hannoon Chapter 7 Utility Tariff Variation and Demand Response Events: A Case Study of Microgrid Design and Analysis������������������������������������������137 Aashish Kumar Bohre and Parimal Acharjee Chapter 8 SOS-Based Load Frequency Controller Design for Microgrids Using Degrees-of-Freedom PID Controller��������������������������������������181 Sunita Pahadasingh, Chitralekha Jena, and Chinmoy Kumar Panigrahi

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Contents

Chapter 9 Performance Improvement of a UPQC Integrated with a Microgrid System Using Modified SRF Technique�����������������������201 Sarita Samal, Prasanta Kumar Barik, and Prakash Kumar Hota Chapter 10 Application of Probabilistic Neural Network and Wavelet Analysis to Classify Power Quality��������������������������������������������������217 Pampa Sinha and Chitralekha Jena Chapter 11 Overview of Security and Protection Techniques for Microgrids�����������������������������������������������������������������������������������231 Asik Rahaman Jamader, Puja Das, Biswaranjan Acharya, and Yu-Chen Hu Chapter 12 Multilevel Voltage-Based Coordinating Controller Modeling, Development, and Performance Analysis�����������������������������������������255 Ranjit Singh Sarban Singh, Tiara Natasya Abdul Halim, T. Joseph Sahaya Anand, and Maysam Abbod Chapter 13 Case Studies on Microgrid Design Using Renewable Energy Sources����������������������������������������������������������������������������������������������279 Arjyadhara Pradhan, Babita Panda, and Rao Mannepalli Index�����������������������������������������������������������������������������������������������������������������������301

Preface Microgrid is a technology that has been introduced to produce self-sufficient and sustainable energy systems. Within the microgrid, there is a diversity of distributed energy systems, such as solar panel systems, wind turbine systems, and combined heat and power systems, as well as many other types of generators that produce power for utilization. Looking at the diversity advantages of microgrids, as well as the growing number of power consumers opting for renewable energy sources, the evaluation of power systems—especially the microgrid type of power system—has become challenging and essential for development improvement. In addition, the growth of emerging power infrastructures opting into designed microgrids has resulted in extensively extending microgrid research and development, not only locally but also internationally. With so much improvement to look at, this book discusses design and development techniques, smart control and switching mechanisms, and innovations that can be used in the future in research or development of microgrids. Furthermore, the combination of diverse research techniques presented in this book will allow for the exploration of new findings involving multidisciplinary approaches in microgrids. With this exploration, it will offer great motivation for authors and editors to bring forth a book that fills the necessary gap as well as introduces new technological microgrid design and development. This text is an international forum for comprehensive researched content matter for design, development, fundamental theories and principles, and control and switching techniques, as well as coordination and management of the proposed power system applications in the environment, energy, electronics, and electric sectors. Researchers, engineers, students, academicians, and scientists of all levels involved in design and development of microgrid power systems will benefit from this book. Readers will gain an understanding of the practical and highly researched area of microgrids for their specific disciplinary areas or fields. The goal of this book is to provide readers with a broad review and presentation of all designs and development, which are highly sensitive findings for microgrid disciplinary research. Therefore, this text provides readers with the entire gamut of smart and intelligent designs, developments, fundamental theories and principles, and control and switching techniques, as well as coordination and management of microgrids. Also, the vision of this book is for the future and moves beyond existing microgrid designs and development to explore new realms of microgrid designs, developments, and applications. Dr. Ghous Bakhsh Narejo Associate Professor, Department of Electronic Engineering, Faculty of Electrical, Computer Engineering, NED University of Engineering & Technology, Pakistan Biswaranjan Acharya Faculty, School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India vii

viii

Preface

Ir. Ts. Dr. Ranjit Singh Sarban Singh Senior Lecturer, Advanced Sensors and Embedded Control (ASECs) Research Group, Centre of Telecommunication Research Innovation (CeTRI), Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Melaka, Malaysia Dr. Fatma Newagy Associate Professor, Electronics Engineering and Electrical Communications, Ain Shams University, Egypt

Editors Ghous Bakhsh Narejo, PhD, is an associate professor in the Department of Electronic Engineering in the Faculty of Electrical, Computer Engineering at NED University of Engineering & Technology in Pakistan. He has 20 years of academic experience in electronic engineering and teaches undergraduate, master’s, and PhD courses related to electronic engineering, microsystem design, industrial electronics, renewable energy, and imaging and analog ICs. He earned his master’s degree in microsystems and electronic engineering from NED University, and his PhD in electrical engineering, specializing in nano devices, from Michigan Technological University, USA. His research is in microgrid design. Dr. Ghous has served in the Electronic Engineering Department at NED University, Karachi, Pakistan for the last 20 years. He supervises multiple PhD students, three of whom have successfully completed their PhDs at NED University. Dr.  Ghous has to his credit numerous research papers. He has authored many research articles published in international journals of repute. Biswaranjan Acharya, PhD, is currently associated with the Kalinga Institute of Industrial Technology (deemed to be university) and is pursuing a PhD in computer application from Veer Surendra Sai University of Technology (VSSUT), Burla, Odisha, India. He received an MCA in 2009 from IGNOU, New Delhi, India and an MTech in computer science and engineering in 2012 from Biju Pattanaik University of Technology (BPUT), Odisha, India. He is associated with various educational and research societies like IEEE, IACSIT, CSI, IAENG, and ISC. With his two years of industry experience as a software engineer, he has a total of ten years’ experience in both academia of reputed universities like Ravenshaw University and in the software development field. He is currently working in the research areas of multiprocessor scheduling in different fields like data analytics, computer vision, machine learning, and IoT. He has 14 patents in his name and another 24 published to his credit. He has published research articles in internationally reputed journals and has served as a reviewer in many peer-reviewed journals.

ix

x

Editors

Ranjit Singh Sarban Singh, PhD, is a senior lecturer in the Department of Computer Engineering in the Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM). He served as a tutor from 2008–2010, lecturer from 2010–2015, and is presently a senior lecturer since 2015. He is also attached to the Advanced Sensors  & Embedded Control (ASECs) Research Group, Centre of Telecommunication, Research  & Innovation (CeTRI) for his research activities. He received his PhD in general control system engineering (renewable energy system management) from Brunel University London, UK. He received his MEngSc in computer engineering (research) from Multimedia University Melaka, Malaysia in 2010. He received his BEng (honours) in electronics engineering (computer engineering) from Kolej Universiti Teknikal Malaysia Melaka (KUTKM), Melaka, Malaysia in 2006. During his service at UTeM from 2008–present, he taught numerous computer engineering subjects such as microprocessor technology, engineering mathematics, microcontroller technology, embedded system design, digital systems, principles of electrical and electronics, and other computer engineering-related subjects. He received his diploma in computer technology from Politeknik Seberang Perai (PSP), Pulau Pinang, Malaysia in 2004. Dr. Ranjit Singh is registered as a Professional Engineer (PE) with the Board of Engineers Malaysia (BEM). He is also registered as a Chartered Engineer of IET (UK), IntPE (UK), and a Professional Engineer (UK), as well as a Professional Technologist (Ts), with the Malaysia Board of Technologists (MBOT). His research interests span several different disciplines: renewable energy system management, battery energy storage management, embedded system design, energy management algorithms, and consumer electronics system design. He has actively pursued research in these fields and published more than 60 journal and conference papers, filed 5 patents nationally, and been granted 1 trademark nationally. He has organized and chaired international conferences and continues to serve as a member of several scientific and organizing committees of international journals and conferences. He continues to review journal and conference papers. He has been a principal researcher for numerous research grants nationally and at universities. He has also won more than ten national and international innovation and invention awards. Dr. Fatma Newagy, PhD, graduated from the Faculty of Engineering, Cairo University, where she worked as a research assistant and obtained a master’s and PhD in electronics and communications engineering. She worked as an assistant professor in Cairo University (2008–2011) until she moved to Ain Shams University to be assistant professor (2011–2016) and associate professor (2017–present). Dr. Newagy is a member of the Space Research and Remote Sensing Scientific Council, Specialized Scientific Councils–Egyptian Academy of

Editors

xi

Scientific Research and Technology (SSC-ASRT). She is a steering committee member at the MIT–ASU Center of Excellence–Energy founded by USAID. She is also a member of the Arab Foundation of Young Scientists. Dr. Newagy has more than 20 years in total academic and research experience in many government and international universities in Egypt. She is an editorial board member on Ain Shams Engineering Journal (ASEJ), Elsevier, and SCIREA Journal of Electronics & Communication, Science Research Association. She is a peer reviewer for many international journals with high impact factors. Dr. Newagy has participated in many research projects and grants. She is the supervisor of Space Innovation Lab in ASU, funded by the Egyptian Space Agency (EgSA). She has more than 60 international research papers, all in international journals with high impact factors, and has participated in international conferences worldwide in wireless communications, smart grids, energy efficiency, satellite communications, IoT, and innovations.

1

Microgrid Design and Challenges for Remote Communities Fawad Azeem, Ghous Bakhsh Narejo, Waleed Rafique, Aizaz Mohiuddin, and Tauseef Anwar

CONTENTS 1.1 Introduction to Islanded Microgrids.................................................................. 2 1.2 Remote Communities and Potential of Microgrids...........................................2 1.3 Current Trends in Design and Architecture of Islanded Microgrids................3 1.4 Different Types of Microgrids and Associated Loads......................................4 1.4.1 Residential Microgrids..........................................................................4 1.4.2 Commercial Microgrids........................................................................4 1.4.3 Special-Purpose Microgrids.................................................................. 5 1.5 Isolated Remote Microgrid Load and Operational Characteristics................... 5 1.6 Design Considerations of Islanded Microgrids for Remote Communities........ 6 1.6.1 Residential Load Model for Remote Communities...............................7 1.6.2 Commercial Load Model for Remote Communities.............................7 1.6.3 Agricultural Load Model for Remote Communities.............................8 1.6.4 PV Model...............................................................................................8 1.6.5 Wind Turbine Mathematical Model......................................................8 1.6.6 Battery System....................................................................................... 9 1.6.7 Transmission System............................................................................. 9 1.7 Islanded Microgrid Management and Control..................................................9 1.7.1 Forecasting Techniques....................................................................... 10 1.7.2 Demand Forecasting............................................................................ 11 1.7.2.1 Long-Term Forecasting......................................................... 12 1.7.2.2 Short-Term Forecasting......................................................... 12 1.7.3 Generation Forecasting........................................................................ 12 1.8 Communication Techniques............................................................................ 13 1.8.1 Communication Methods and Technologies Available for Microgrid Systems............................................................................... 13 1.8.1.1 Power Line Communication................................................. 13 1.8.1.2 Zig-Bee.................................................................................. 13 1.8.1.3 Cellular Network Communication........................................ 13

DOI: 10.1201/9781003121626-11

2

Microgrids

1.9 AC/DC Inverters and Power Electronics Used in Islanded Microgrids.......... 14 1.10 Optimal Sizing of Islanded Microgrids........................................................... 14 1.10.1 Single Algorithm................................................................................. 14 1.10.2 Classical Technique............................................................................. 15 1.11 Economics of Microgrids and Revenue Generation Methodologies............... 15 1.11.1 Monthly Payment Plans....................................................................... 15 1.12 Isolated Community Microgrid Challenges.................................................... 15 1.12.1 Technical Challenges........................................................................... 16 1.12.2 Social Challenges................................................................................ 16 1.13 Future Prospects.............................................................................................. 16 References���������������������������������������������������������������������������������������������������������������� 17

1.1 INTRODUCTION TO ISLANDED MICROGRIDS Islanded microgrids, as the name implies, are isolated systems responsible for self-generation, transmission and distribution [1]. Microgrids are low-voltage systems with a defined load near a generation system. There are two major types of microgrids: islanded and grid-connected networks. A grid-connected microgrid is linked with the mainstream network via a point of common coupling (PCC), where islanded systems are self-sustainable systems. Renewable energy–based distributed generation is mostly used in recent microgrids for production of cleaner energy and to satisfy growing environmental concerns. With increasing penetration of renewable energy–based distributed generation sources, issues related to power quality and stability need special attention. Voltage, current and frequency deviation issues are well addressed in grid-connected microgrids, whereas islanded microgrids are responsible for self-control, which makes it more challenging compared to grid-connected systems. To address stability issues, battery storage devices, diesel generators and other storage devices such as super capacitors and so on are used. However, battery storage devices are mostly used for power conditioning and stability control. The major component in an islanded microgrid system consists of control of the microgrid. The controller is responsible for ensuring the stability and economic operation of microgrids while controlling the power dispatch from the battery, distributed generation sources, load management and optimal power flow during the night hours or during undesirable events such as rainy/cloudy days or any event that reduces power output from distributed generation sources. Figure 1.1 shows major components of an islanded microgrid.

1.2 REMOTE COMMUNITIES AND POTENTIAL OF MICROGRIDS Nearly one billion people live without electricity [1–3]. The living standard in such communities is low and lacks several basic necessities such as state-of-the-art hospitals, schools and other requirements of daily life. However, microgrids equipped with green energy resources serve as potential candidates to invest in rural electrification [4]. Rural electrification using intermittent distributed generators has shown

3

Microgrid Design for Remote Communities

Distributed Generation

Solar Power

Wind Power

Hydel

Controller

Load Management

Economic Dispatch Management and Control

FIGURE 1.1  Islanded microgrid major components.

good potential for electrifying unprivileged areas with green energy. There is considerable growth in installation of the off-grid systems using solar and wind energy in developing countries. These systems help run residential areas, small businesses such as shops, small processing plants and community places such as hospitals, places of worship and schools. However, the intermittent nature of major sources of generation like solar and wind energy causes challenges such as severe power quality issues like voltage sags and frequency issues that eventually lead to blackouts [5, 6]. There is ongoing research to provide quality power to renewable energy–based islanded microgrids using different configurations, control algorithms, energy and demand management and utilization of storage systems for economical and reliable operation of islanded communities. The structure of the microgrids plays an important role in the reliable operation of microgrids and heavily depends on the nature of the load that runs in the main target area. The next section discusses the design and architecture of microgrid implementation in remote communities on the basis of their loads and applications.

1.3 CURRENT TRENDS IN DESIGN AND ARCHITECTURE OF ISLANDED MICROGRIDS The basic architecture of an islanded microgrid is given in Figure 1.2. The architecture shows the major constituents of the microgrids, including solar photovoltaic (PV) distributed generators [1, 7, 17, 18], AC wind turbine generators, inverters connected with a DC PV power source and the whole generation source connected

4

Microgrids

FIGURE 1.2  Microgrid architecture.

to an AC bus bar. To further enhance the reliability and power quality, the battery storage is configured to be charged from solar power and provide power to the load. The battery storage is responsible for frequency and voltage regulations and also provides power during the night hours. The load, storage and distributed generation is connected to the centralized controller. The centralized controller is the heart of the islanded microgrid and performs load management, economic and appropriate power dispatch from storage using feedback systems. The decisions are based on the available past, current and future information using forecasting, sensory information load and weather profiles of the area.

1.4 DIFFERENT TYPES OF MICROGRIDS AND ASSOCIATED LOADS Based on the application and needs, microgrids can be of different types with different load applications. Power utilization with respect to loads is different between the various microgrids. Some common microgrids being utilized are given in the following.

1.4.1 Residential Microgrids Residential microgrids are the most common type of microgrid being used to feed the domestic load. The most common loads for residential microgrids are fans, TVs, LEDs, refrigerators and other household loads. Isolated communities often exist in rural areas of developing nations that utilize load during the night hours. That is why load utilization characteristics are different and depend heavily on battery storage. The cost of battery storage increases the overall capital cost of the system.

1.4.2 Commercial Microgrids Commercial microgrids are used to provide power to agricultural, industrial and other commercial loads such as shops and so on. The load in such microgrids usually operates during the daytime, while a minimal residential load runs during the night hours. However, the agricultural loads are heavy compared to residential microgrids.

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Microgrid Design for Remote Communities

The operation and maintenance cost for such loads is comparatively lower than for residential microgrids due to lower battery storage requirements.

1.4.3 Special-Purpose Microgrids Special-purpose microgrids are those that run remote military bases, campuses, hospitals and so on, where access to the mainstream network is not available. The loads are mostly similar to residential microgrids, including some special-purpose loads. The utilization of loads is mostly uniform during the day and night. To provide reliability and avoid blackouts, such microgrids uses diesel generators to provide backup power for sensitive loads.

1.5 ISOLATED REMOTE MICROGRID LOAD AND OPERATIONAL CHARACTERISTICS Table 1.1 shows the operational characteristics of three types of microgrids and their utilization in isolated areas under different conditions. A  residential microgrid is equipped with storage systems that increase the overall capital cost. Unlike remote residential microgrids, agricultural microgrids normally work during the daytime and utilize directly available power, in most cases solar energy to feed heavy loads; this reduces the cost for battery storage, which eventually reduces the capital cost of the system. Special-purpose microgrids use loads that run 24 hours and require storage as well as diesel generators in most cases for reliability. The capital cost for special-purpose microgrids is high compared to residential and commercial microgrids. Once residential microgrids are installed in rural isolated communities, the availability of skilled manpower is not guaranteed on permanent basis. The same is the case with commercial microgrids. On the other hand, special-purpose microgrids are organized with maintenance, fault detection and troubleshooting staff available within the microgrid system staff. The financial liabilities of commercial and residential microgrids are on the owners, whereas the government mostly takes responsibility for special-purpose microgrid financial liabilities. These liabilities include capital costs, operation and maintenance costs and other required costs.

TABLE 1.1 Operational Characteristics of Islanded Microgrids Sr #

Characteristics

Residential Microgrids

Commercial Microgrids

Special-Purpose Microgrids

1

Load Operation

16–18 hours

8–10 hours

24 hours

2

Capital Cost

High

Low

High

3

Skilled Manpower Availability

Yes

No

Yes

4

Special-Purpose Loads

No

No

Yes

5

Financial Liabilities

Owner

Owner

Government

6

Social Benefits

Yes

Yes

Yes

6

Microgrids

1.6 DESIGN CONSIDERATIONS OF ISLANDED MICROGRIDS FOR REMOTE COMMUNITIES Design of the microgrid is layered in three interconnected systems to perform operation in islanded mode. Figure 1.4 shows the layers of islanded microgrids in remote communities. These design layers support the controller in making effective decisions such as introducing load shedding, taking power from storage, charging storage and initiating demand management controlling responsive loads. All such actions are taken to reduce the frequency and voltage disturbances and to reduce potential blackouts. The layers are based on previous information available such as load and generation profiles and are considered permanent information. Permanent information is not adaptive and does not change with the passage of time. Permanent information may include the load profiles of the area, interviews with the community people and weather and climatic conditions of the area based on which distributed generators are selected. Permanent information helps in the initial design and appropriate system sizing. The second layer is the adaptive layer that takes information from sensors, instantaneous load consumption, market prices, current weather information and availability of the state of the charge of storage. The third layer is the decision and optimization layer that decides on the appropriate action based on the second layer. The operation of the third layer heavily depends on the information received from the second layer, that is, the adaptive layer. The decision is either made based on the availability of the adaptive data or the decision layer optimizes the values for better operation of the system. In the following sections, the design parameters of each layer will be studied in detail for the appropriate operation of islanded microgrids in remote communities.

Permanent Information Layer Forecasting

Adaptive Information Layer Sensor output Market prices

Load profiles

Load consumption Trends

Decision Layer Optimization

Current weather profiles

FIGURE 1.3  Layers of remote islanded microgrid.

Decision for Generation and Load Control

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Microgrid Design for Remote Communities

1.6.1 Residential load Model foR ReMote coMMunities The residential sector plays a vital role in energy demand. Currently, consumers constantly use more electrical appliances even in remote communities compared to the load trends in the last decades. It is due to an improved lifestyle. Domestic appliances can be categorized into brown goods and white goods. Small electronic consumer appliances are known as brown goods. Office and other communication equipment such as laptops, LCDs, printers, scanners, phones, routers and so on that are not part of small marketplaces in remote communities are also included in brown goods. White goods are also known as white-ware. This class has appliances like kettles, toasters, fans, irons, sewing machines, vacuum cleaners and so on that are used in daily routines such as cooking, cleaning and preservation [8]. However, the load contribution of white goods is still low in remote communities. Lighting also play crucial role in energy consumption. In the residential load model for the isolated communities, the load types that have considerable and continuous usage in the daily routine are taken into account. Equation (1.1) shows the residential loads. n n n  n  LR ( t ) = P(t) ∑PTV + ∑PRef + ∑PL + ∑PF   1  1 1 1

( )

(1.1) n

where P is the instantaneous load running at time t and ∑PTV is a summatio n (t ) n n 1 of the television load. ∑PRef denotes the refrigeration load, ∑PL shows the lighting load and

n

1

∑P

shows the load of fans.

F

1

1

1.6.2 coMMeRcial load Model foR ReMote coMMunities The commercial sector covers electrical loads that are mainly used in restaurants, shopping centers, malls and so on. Load consumption is higher during the day as compared to the residential load. Commercial loads usually have non-linear consumption patterns. These loads may produce power quality issues such as harmonics, voltage dips and frequency deviations. The commercial loads in remote communities are mainly made up of fans, laundry and lighting loads with some heavy water pump operation for irrigation purposes. Equation (1.2) shows commercial loads for remote communities. n n n  n  LCom ( t ) = P(t) ∑PWp + ∑PWM + ∑PL + ∑PF   1  1 1 1

( )

where

n

n

∑P , ∑P

WM

wp

1

1

n

,

∑P

L

1

and cooling (fans) loads.

n

(1.2)

+ ∑PF refer to the water pump, washing machine, light 1

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Microgrids

1.6.3 agRicultuRal load Model foR ReMote coMMunities Agricultural load includes activities that are mostly related to irrigation. Motors in agriculture are mainly used for water pumping. Agricultural loads may vary with the seasons (environment, weather change), for example, during a rainy day, there will be no or very low utilization of solar pumps to draw power for irrigation. The power required by agricultural sector is mainly used for activities such as planting, pumping, cultivating and so on. The inductive nature of water pumps may affect power stability during the operation of other parallel loads and hence may affect power quality. Switched capacitors and voltage regulators are often used to solve power quality issues [9].

1.6.4 pV Model A PV cell is the basic unit of a PV system. Many PV cells combined together form a module. To get the required voltage, many modules are connected in series and parallel. PV cells convert sunlight into electrical energy. The output current from the equivalent model is described by Equation (1.4).    Vpv −IpvRs    Vt    Vpv − RsIpv  − 1 − Ipv = Ncell Isc − Io (e    Rp   

(1.4)

In the equation, as discussed in [10], Isc represents a short circuit current under the given irradiance, the diode current is represented as Id, series resistance is Rs and Io is the diode saturation current. PV voltage is changed by switching the special design DC-DC converter to get the maximum average power output of the array [10].

1.6.5 Wind tuRbine MatheMatical Model Wind turbines generate mechanical torque from wind in the form of kinetic energy and deliver it to a rotor for mechanical output that turns the generator shaft Tm to produce power Pm [11, 16]. The power obtained from wind is given as in [11]. 1 Pw = C p (λ ) *ρAV 3 2

(1.5)

In this expression, ρ represents air density (kg/m3), wind speed is represented by V, and A denotes the area of the blade. λ represents the tip speed of the turbine [11]: λ=

ωR V

(1.6)

In Equation (1.6), ω represents the blade angular velocity, R represents the radius and V is the velocity of the wind.

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9

From the value of power, we can find the mechanical torque on the shaft as [11]: Tm =

Pm

(1.7)

ω

where Tm is the mechanical torque, Pm is the power and ω is the blade angular velocity.

1.6.6 batteRy systeM The mathematical model of the battery is given in Equation (1.8) [10]. Eb = Eb,o − K

Qb (−B .Ib,t ) + Ae . Qb − Ib,t

(1.8)

where Eb is the open circuit voltage, Eb,o is the open circuit voltage under standard conditions, Qb is the battery charge capacity, A and B are the constants linked to exponential terms, t is the time and lb is the time integral of the battery current [10].

1.6.7 tRansMission systeM Distributed generators are connected to designated loads via a low-voltage transmission system. The reactive power compensation is done using the storage system, as given in Equation (1.9) [12], where ΔP, ΔQ control the changes in power (active and reactive) generation/consumption. On the other hand, storage systems are also used for active power delivery during undesirable events as well as during the night hours. U 2 ≈U1 +

Rline Pder − PL ± ∆Pcontrol  + Xline[Qder − QL ± ∆Qcontrol U2

(1.9)

1.7 ISLANDED MICROGRID MANAGEMENT AND CONTROL Microgrid management and control lie in the second layer of isolated microgrids and can be considered the heart of reliable operation. Figure 1.4 shows the hierarchy of microgrid management and control. Information sharing between microgrid components and controller, sensory information gathering and appropriate actuation such as controlled power dispatch and load shedding decisions are meant to be made based on the operation of the second layer. The complete islanded microgrid operation is based on robust control and information being transmitted at a particular instant of time. Adaptive information along with forecasting results provide a basis for decisions based on the microgrid control objectives. For instance, the control algorithm aims to monitor the load and perform load shedding, and parameters such as short-term load forecasting, load consumption and state of the charge of storage parameters may be taken into account. The same is the

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Microgrids

FIGURE 1.4  Microgrid design and implementation consideration for remote communities.

case with generation control, load scheduling, demand response and controlled power dispatch from storage. Frequency and voltage regulations are also termed control parameters and lie within the adaptive second layer of microgrid controls. Additional weather information is dependent on the availability of the internet. However, remote communities often do not have the option of quality internet service, especially in islanded rural areas of developing countries. Second-layer information is routed using communication channels and protocols to the third layer. The third layer is responsible for taking action based on the control decisions made. The third layer is the actuation and control layer and is responsible for obeying the decisions being made by the controller based on the second layer’s information.

1.7.1 Forecasting Techniques Forecasting lies in the first layer of microgrid design. Forecasting techniques are used to evaluate and analyze the demand and supply patterns under different conditions and events. Forecasting is essential for initial microgrid planning, especially for islanded microgrids. Figure 1.5 shows different types of forecasting techniques

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Microgrid Design for Remote Communities

Long-Term Load Forecasting

Generation Forecasting

Microgrid Planning and Control

Short-Term Load Forecasting

FIGURE 1.5  Types of forecasting used for microgrid planning and control.

used for microgrid implementation and planning. Formally, supply-side forecasting is known as generation forecasting, whereas demand-side forecasting is known as load forecasting. Several techniques are utilized for load and generation forecasting.

1.7.2 Demand Forecasting Load forecasting is done to analyze the future load patterns of the community, which gives an idea of appropriate system sizing. Practically, accurate load estimation for future utilization gives appropriate overall system sizing. Load forecasting is also essential for financially viable systems. In isolated remote communities, the

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TABLE 1.2 Parameters Required for Different Types of Forecasting Parameters for Short-Term Forecasting

Parameters for Long-Term Forecasting

Forecasting Techniques

Load consumption

Load type

Regression analysis

Time of operation

Time of operation

Artificial intelligence

State of the charge of storage

Total power

Fuzzy logic

Available generations for current and next day

Time of day

Multi-criteria decision-making

Schedule of responsive loads

Holidays

Fuzzy-neuro hybrid forecasting

Market prices

Day of the month

exact load consumption trends are often not available. In such cases, load trends of a nearby community having similar activities and residence style can be taken as a reference. On the other hand, having knowledge of some parameters such as types of load to be operational, their time of use and so on helps in estimating load trends. Load trends for isolated remote communities can also be gathered using interviews with residents. There are two dominant forecasting types used: 1) long-term forecasting and 2) short-term forecasting. Both forecasting methodologies have different purposes. 1.7.2.1 Long-Term Forecasting Long-term forecasting is used for microgrid planning purposes. The forecasting results are used to evaluate system sizing in the future using the parameters that affect the overall consumption of the system. Since load trends in remote communities are not much different and variations in consumption patterns are low compared to urban areas or highly dense areas, the parameters used for forecasting load trends can be trusted for overall microgrid planning and system sizing. 1.7.2.2 Short-Term Forecasting Contrary to long-term forecasting, short-term forecasting methods are used especially for day-ahead load consumption trends. These trends are used to evaluate variations in the loads and adjust accordingly to get rid of blackouts or emergency conditions. Short-term forecasting is helpful in decision-making processes for controllers. Table 1.2 shows the parameters and techniques used in long- and short-term forecasting.

1.7.3 Generation Forecasting Generation forecasting is used to predict the generation trends of distributed generation based on the climatic conditions of the area. It is essential to forecast the generation trends of the area for feasible microgrid operations. Generation forecasting

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13

is dependent on the availability of the climatic conditions of the area such as annual precipitation, number of cloudy days in the area, sunny days, average wind speed, wind gusts and availability of water resources in the case of hydel energy. Generation forecasting parameters are available on an accessible website that helps in evaluating the site while performing generation forecasting on a long-term basis. Generation forecasting is not only required for a long-term basis, but it also effectively helps in short-term planning such as day-ahead planning to decide on the operation of responsive loads, utilizing battery storage during day- or nighttimes and making decisions for load shedding.

1.8 COMMUNICATION TECHNIQUES Communications systems play a vital role in microgrid (MG) systems. The communication system provides a connection with all units in the MG. The central controller (CC) must coordinate with all units to manage the microgrid. A  local data concentrator is usually used to collect data from several groups of nodes and then send the data to the CC via a communication channel. Many technologies and channels for communication can be used in microgrids to ensure stability in the microgrid.

1.8.1 Communication Methods and Technologies Available for Microgrid Systems A communication system is needed for power control, protection and restoration in a MG [13]. The basic methods of communication used in MGs are programmable logic controller (PLC), broadband over a power line, global systems for mobile communication, local-area network (LAN), wide-area network (WAN), TCP/IP, fiber optic, Wi-Fi 802.11b and Zig-Bee/IEEE 802.15.4. 1.8.1.1 Power Line Communication PLC is a method that uses power lines to transfer high-speed (usually 2 to 3 Mb/s) data signals from one piece of equipment to the other. PLC uses power cables as communication media and today has received attention for smart power systems. 1.8.1.2 Zig-Bee Zig-Bee is a wireless communications technology. The advantage is relatively low in usage of power, high data rate and complexity. It is a suitable technology for smart lightning systems, energy monitoring systems, home automation, automatic meter reading and so on. 1.8.1.3  Cellular Network Communication Cellular networks can be the best option for communicating between direct grids and the utility. The existing communications system prevents utility operational costs and the additional time frame for making a strong communications infrastructure.2G, 2.5G, 3G and Wi-MAX are the advanced technologies available for microgrid systems [14].

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1.9 AC/DC INVERTERS AND POWER ELECTRONICS USED IN ISLANDED MICROGRIDS The role of the smart inverter in islanded microgrids is to operate as an interface between power generation and consumption points. The basic function is not only to convert AC to DC or vice versa but also to control the power flow and to disconnect the system in case of any fault. The processing components of a MG are an inverter that collects data and automatically configures to help operate in a safe mode. The power converter acts as an interface for a distributed energy resource (DER) to the grid whose objectives are to fulfill the requirements of the IEEE 1547 standards series. Grid-forming inverters have main objectives that are first grid connection and second islanded operation. In the case of grid connection, inverters control the power (active, reactive) injected into the AC bus to balance the state of charge for islanded operation. It is compulsory to generate voltage that is purely sinusoidal in the bus. Grid inverters in the form of voltage source inverters mainly help in this task. In islanded mode, grid inverter control is typically changed to operate as a voltage-controlled source; these inverters can be also controlled to follow the grid. It also injects a given amount of current that helps grid-forming converters. In the case of grid-connected operation, the supporting inverters do not work or may be used to improve the power quality of the supply bus.

1.10 OPTIMAL SIZING OF ISLANDED MICROGRIDS The planning and implementation phase for an islanded microgrid requires optimal sizing of the overall microgrid [15]. Optimal sizing is essential for the initial or capital investment cost of the microgrid that includes generation, transmission, distribution, installation and storage cost. It is most important to find out the appropriate sizes of renewable energy sources and the associated energy storage for reliable, efficient operation of the MG. Optimal sizing techniques can be categorized into classical or modern techniques. Classical methods use iterative, numerical, analytically and probabilistic and graphical construction techniques. These techniques use differential calculus to find the optimum solution. Modern methods use artificial methods. These techniques determine the global optimum system. A  commonly used software tool in optimal sizing for standalone hybrid energy systems (HES) is the hybrid optimization model for electric renewables (HOMER). Other software, IHOGA, has been used in optimal sizing for standalone HES. Sizing optimization methods can use a single-objective optimization function and multi-objective optimization functions.

1.10.1 Single Algorithm Single algorithms, which have classical and artificial techniques used to solve size optimization for photovoltaic-wind turbine hybrid energy systems, are reviewed in the following.

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15

1.10.2 Classical Technique Rare studies have been carried out in classical methods for optimal sizing of microgrids. Algorithms such as DIRECT are an efficient technique used to find the global optimum of many problems. Some studies have been carried out in linear programming (LP) in optimizing the size of standalone HESs. Mixed integer linear programming (MILP) takes into account the demand of energy at consumption points and maps to find the location and optimal size of the HES. The main function of optimization is to reduce the initial system cost that is used to find the performance of system. Studies found that the optimal location, in addition to optimal size, minimizes the initial costs.

1.11 ECONOMICS OF MICROGRIDS AND REVENUE GENERATION METHODOLOGIES Islanded microgrid systems in remote communities need financial models to be deployed to build a self-sustainable system. Such communities cannot afford heavy investments, but the subsidy from the government is not realistic. This requires a unique financial revenue collection methodology for repair, maintenance and component replacement such as battery storage systems. The system to be deployed needs a revenue collection methodology that does not place a burden on the residents and serves the purpose of self-sustainable microgrid systems. A revenue generation methodology is developed in this chapter in the following.

1.11.1 Monthly Payment Plans Monthly payment plans are designed on a per-capita basis. This means the number of people living in any house will have to pay the bill. The overall per capita can be evaluated based on the capital and recurring cost of the entire system divided by the total number of people living in that area. Xc = MC + Rc / P (1.10)

where Xc is the per capita cost, Mc is the microgrid capital cost, Rc is the recurring maintenance cost and P is the number of people residing in the area. For instance, if the total cost of the microgrid is 20,000 USD and the recurring maintenance cost is 500 USD/month and 6000 USD/year, and the number of people who benefit from the microgrid is 1000, then cost per capita will be 26 USD. Each household will have to pay 26 USD for a year to generate revenue of 26,000 USD per year and 2166 per month. Each participant will be bound to pay 26 USD a year, which is equivalent to 2.16 USD/month and is a nominal amount to pay easily.

1.12 ISOLATED COMMUNITY MICROGRID CHALLENGES According to [18], isolated microgrid communities, especially in remote areas, face the challenge of 4As: accessibility, affordability, adaptability and applicability. The

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challenge lies in both technical and social issues for effective, sustainable, efficient and reliable microgrids. The technical and social challenges faced by the remote communities while implementing microgrids are given in the following.

1.12.1 Technical Challenges • Research work on microgrids is mostly done at the lab scale under a controlled environment where the real ground issues are not addressed. Hence, most studies are based on the lab scale or simulations. • There is a need to develop a robust communication infrastructure to deal with the control system that is the heart of the microgrid. In remote communities, internet to get real-time weather data is not available. On the other hand, sophisticated communication protocols are either established in urban areas or only at lab-scale implementation. • There are no load generation trends available that are needed for optimal microgrid planning. • The designed controller needs proper monitoring and sensory information; maintenance of the control system, sensors and communication system is an issue. • Modern wireless communication, weather profiles and control systems are not accessible.

1.12.2 Social Challenges • The cost of expensive microgrid components and generation systems is beyond the ability of the local people. • Lack of education is the biggest hurdle, due to which local manpower cannot be deployed to maintain the control system and so on. • The people do not accept change. Load consumption and scheduling is not properly followed. • There is no technical manpower available to check if the system malfunctions. • There is no proper revenue generation methodology that helps in selfsustainable system installation.

1.13 FUTURE PROSPECTS The aging transmission and distribution network foundation is among the significant obstacles in planning a reliable and economically viable power system. The upgrade of the current transmission foundation needs huge investments, which are practically unrealistic. This circumstance has provided a reasonable opportunity for renewable power sources connected to a low transmission framework. Microgrids are developing all around the globe, especially in islanded areas that are not connected to mainstream networks. The development of the microgrid market has expanded exponentially since the year 2011 and is anticipated to reach an introduced limit of over 15 GW by 2022. Today, grounds or institutional microgrids

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are the biggest by application and are estimated to develop at a compound annual development rate (CAGR) of 18.83% from 2012–2022. It is obvious from the current situation and ideal conditions for microgrids that in the future, microgrids will fulfill unconnected areas’ demands in developed as well as developing nations.

REFERENCES 1. Narejo, G. B., Azeem, F., & Ammar, M. Y. (2015, June). A survey of control strategies for implementation of optimized and reliable operation of renewable energy based microgrids in islanded mode. In 2015 Power Generation System and Renewable Energy Technologies (PGSRET) (pp. 1–5). IEEE. 2. Azeem, F., Narejo, G. B.,  & Shah, U. A. (2018). Integration of renewable distributed generation with storage and demand side load management in rural islanded microgrid. Energy Efficiency, 1–19. 3. Azeem, F.,  & Narejo, G. B. (2020). A  fuzzy based parametric monitoring and control algorithm for distinctive loads to enhance the stability in rural islanded microgrids. Facta Universitatis, Series: Electronics and Energetics, 33(2), 227–241. 4. Fowlie, M., Khaitan, Y., Wolfram, C.,  & Wolfson, D. (2018). Solar microgrids and remote energy access: How weak incentives can undermine smart technology. TechRep. Energy Institute at HAAS. 5. Blair, N., Pons, D.,  & Krumdieck, S. (2019). Electrification in remote communities: Assessing the value of electricity using a community action research approach in Kabakaburi, Guyana. Sustainability, 11(9), 2566. 6. Ustun, T. S., Ozansoy, C.,  & Zayegh, A. (2011). Recent developments in microgrids and example cases around the world—A review. Renewable and Sustainable Energy Reviews, 15(8), 4030–4041. 7. Nguyen, X. H.,  & Nguyen, M. P. (2015). Mathematical modeling of photovoltaic cell/module/arrays with tags in MATLAB/Simulink.  Environmental Systems Research, 4(1), 24. 8. Dickert, J., & Schegner, P. (2010). Institute of electrical power systems and high voltage Engineering Technische Universität Dresden, Dresden, Germany. “Residential Load Model for Network Planning”, Conference Paper Modern Electric Power Systems, Wroclaw, Poland. 9. Ali, F., Naeem Arbab, M., & Kashif Khan, M. (2019). Department of electrical engineering, US-PCASE UET Peshawar Pakistan, “SVC based voltage stabilization for sensitive agricultural loads”. Sarhad Journal of Agriculture, 35. 10. Giorgio Caua, Daniele Coccoa,  & Mario. (2014). Modelling and simulation of an isolated hybrid micro-grid with hydrogen production and storage. Department of Mechanical, Engineering, University of Cagliari, via Marengo 2, Conference Italian thermal Machine Associates, Elsevier. 11. Worku, M. Y., & Abido, M. A. (2017). PMSG based wind system for real-time maximum power generation and low voltage ride through. Journal of Renewable and Sustainable Energy, 9(1), 013304. 12. Krechel, T. S. F., Gonzalez, L., Chamorro, H.,  & Rueda, J. L. (2019). Transmission system-friendly microgrids an option to provide ancillary services. 2019 IEEE Milan Power Tech Italy, Elsevier, doi:10.1016/b978-0-12-817774-7.00011-9. 13. Mohagheghi, S., & Yang, F. (2011). Applications of microgrids in distribution system service restoration. ISGT, doi:10.1109/isgt.2011.5759139. 14. Mr. Chavan, P. D., & Prof. Devi, R. J. (2016). Survey of communication system for DG’s and microgrid in electrical power grid. International Research Journal of Engineering and Technology, 3(7).

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Al-falahi, M. D. A., Jayasinghe, S. D. G., & Enshaei, H. (2017). A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system. Energy Conversion and Management. ISSN: 0196–8904. 16. Wang, C. N, Lin, W. C., & Le, X. K. (2014). Modelling of a PMSG wind turbine with autonomous control. Mathematical Problems in Engineering, doi:10.1155/2014/856173. 17. Patrao, I., Figueres, E., Garcerá, G., & González-Medina, R. (2015). Microgrid architectures for low voltage distributed generation.  Renewable and Sustainable Energy Reviews, 43, 415–424. 18. Azeem, F., Narejo, G. B.,  & Shah, U. A. (2018). Integration of renewable distributed generation with storage and demand side load management in rural islanded microgrid. Energy Efficiency, 1–19.

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Microgrid Design Evolution and Architecture Kumari Namrata, Ch Sekhar, D.P. Kothari, and Sriparna Das

CONTENTS 2.1 Introduction..................................................................................................... 19 2.2 History of Microgrids...................................................................................... 22 2.3 Need for Microgrids........................................................................................ 23 2.4 Definitions of Microgrids................................................................................25 2.5 Structure of Microgrids...................................................................................25 2.5.1 Distributed Generation........................................................................26 2.5.1.1 Stand-Alone Reciprocating Engine Generators....................28 2.5.1.2 Turbine Generators...............................................................28 2.5.1.3 Solar PV Cells....................................................................... 29 2.5.1.4 Wind Turbines....................................................................... 30 2.5.1.5 Fuel Cells.............................................................................. 30 2.5.2 Energy Storage Technology................................................................. 31 2.5.3 Microgrid Loads.................................................................................. 33 2.6 Operation of Microgrids.................................................................................. 33 2.7 Monitoring of Microgrids................................................................................ 35 2.8 Stability, Control, and Strategies for Microgrids............................................. 36 2.8.1 Microgrid Control Techniques............................................................. 41 2.9 Interface Modules of Microgrids..................................................................... 41 2.10 Types of Microgrids......................................................................................... 43 2.11 Pros and Cons of Microgrids...........................................................................44 2.12 Microgrid Architecture.................................................................................... 45 2.13 Commercial Planning of Microgrids............................................................... 48 2.14 Conclusion....................................................................................................... 48 References���������������������������������������������������������������������������������������������������������������� 48

2.1 INTRODUCTION Human beings have been very fond of knowledge, innovative ideas, inventions and discoveries from the very beginning. One of the most successful and powerful DOI: 10.1201/9781003121626-219

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discoveries was the discovery of electricity through a lightning experiment conducted by Benjamin Franklin in the year 1752. Later, many experiments were conducted, and this led to invention of the light bulb by Thomas Edison and the AC generator by Nikola Tesla, and then the development of the power grid took place. The age of electricity in the United States began during the 1890s with the lighting of the Chicago World’s Fair and completion of the first long-distance transmission line bringing hydro-electric power from the mighty Niagara Falls to the city of Buffalo [1]. The Industrial Revolution also supported the evolution of electricity by machines and other tools. The development of grids is still going on, which bought the introduction of the concept of the smart grid to twenty-first-century society. The microgrid plays a vital role in the formation of smart grids. In fact, without microgrids, the concept of building a smart grid is incomplete. So the study of microgrid concepts, design, evolution and architecture is important for an uninterruptible power supply to every customer connected to the main grid [2]. Knowing the meaning of electric grids is needed before discussing microgrids and smart grids. Most of us are well aware of grid systems. The electric grid or power grid is an interconnection of generating and distribution systems through transmission systems. The generation system consists of generating stations where electricity is generated and transmitted to the consumers in a distribution system. Distribution systems have different types of customers who require electric power. The transmission system consists of different levels of subsystems and transmission lines. These definitions are related to the present scenario where one-way communication is present, and a typical layout of an electrical power grid can be seen in Figure 2.1. One-way communication is the process of getting information related to problems in power utilization from the customer to the generating stations, but the customer is not well informed about the status of problem solving from the generating station. This problem can be solved by installing smart metering units and computers at both ends, and then two-way communication will be possible [3, 4].

FIGURE 2.1  A typical layout of an electrical power grid.

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Microgrid Design Evolution and Architecture

Until now, research models have been used to check the operation and efficiency of smart grids. The main challenge in electric power supply is to supply efficient power continuously without any interruptions and free from harmonics to the customer or industries. This problem can be minimized or solved by introducing a distributed generation unit near the customer or by the customer and connecting it to the main grid [5]. Distributed generation can be done by a standalone generator, a PV system, windmill system, fuel cell generators and so on. The concept of distributed generation units in the distribution system and connecting them to the main grid brings in the formation of microgrids. Distributed generation units or distributed energy resources can be defined as the devices used as electric supply, but these are not widely deployed in the main grid or in a centralized grid and are connected at distribution or lower voltages to meet the demand [6]. For example, PV arrays and wind turbines are used with battery storage so that electric power can be available even for non-emergency use like turning on lights, fans, and so on. Microgrids can be assumed to be a minor form of an actual grid because they are similar to a conventional grid with respect to the terms of power generation, transmission and distribution of power with control features [7, 8]. However, the technology of microgrids differs from technology of conventional grids with respect to the distance between power-generating sources and consumers, and it also differs with the consumption cycles because the installation of a microgrid is near the consumer or load sites. Table 2.1 shows a comparison between microgrids and conventional grids.

TABLE 2.1 Microgrids versus Conventional Grids Microgrid

Conventional grid

Generation

Micro generation or distributed generation

Large-scale generation or central generation

Transmission losses

Lower losses

Higher losses (in India, around 26% is lost in aggregate technical and commercial

Changes occurring in grid

The transmission load is reduced, so the need for grid upgrades is lower

The transmitted power is increased, so there is a need for grid upgrades

Waste heat as byproduct

Can be used for heating purposes, commonly known as micro combined heat and power

Used in privately owned industrial combined heat and power (CHP) installations

Reliability and management

Extremely reliable and managed by an individual, a group of customers or small industries

Managed by power company and government

Future development

Focused on the greenness of energy

Focused on energy crisis

Ability to meet needs

Renewable energy resources are able to produce lesser than nameplate capacity

Fuel-based or hydro-power generation is fully dispatchable

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2.2 HISTORY OF MICROGRIDS The history of microgrids started in 1955 at the Whitling refinery, which is located in Indiana, where the first modern industrial microgrid with a 64 MW facility was constructed in the United States per Pike Research. But it was not where the first microgrid was spotted; rather, the concept of a microgrid started with the beginning of industry. In the year 1882, no standards for a generation and distribution system had been set for electricity, so Thomas Alva Edison designed his Pearl Street Station as he went along [8]. The station was situated at 255–257 Pearl Street in Manhattan, and the station was powered by coal. Surprisingly, the design of Edison’s Pearl Street station met all of today’s criteria for a microgrid system. The coal-fired steam engines in the station drove six jumbo generators, and each generator was able to produce 1100 KW DC. On September 4, 1882, the Pearl Street Station began operating, and it provided electricity to 508 customers who were present in New York, and a total of 10,164 lamps were provided with electricity. The byproduct of the plant was found to be steam, so Edison also made the use of this steam by providing it to local manufacturers, and the heat produced from the steam was provided to nearby buildings [8]. It can be seen that the electric system created by Edison was a small localized generation unit with a distribution network that was limited to a small area, a perfect replica of today’s microgrid definition and structure. Due to the restrictions of the DC transmission system, Edison’s system was able to serve only a few blocks in any direction, and his system also included storage batteries. This concept gave rise to the idea of microgrids with energy storage and interestingly almost all today’s research deals with this topic. Today we use an alternating current (AC) grid, which makes the biggest difference observed from the electric system built by Edison because he used a direct current (DC) grid. DC grids were able to work only in densely populated areas like Lower Manhattan, as shown in Figure 2.2, and the voltage levels were low. It was nearly impossible to implement a DC grid on a wider scale. So, for long-distance transmission, the AC grid built by Nikola Tesla proved the best solution in 1893 at the Chicago World’s Fair, which shaped history as we know

FIGURE 2.2  Pearl Street Station and Lower Manhattan, which was lit up by Edison.

Microgrid Design Evolution and Architecture

23

FIGURE 2.3  Edison’s DC grid connection for Pearl Street station.

it today. Today’s AC grids are the outcome of that history. But the power demand is increasing day by day, and customers expect an uninterrupted power supply, which inspired the idea of microgrids, and the outcome of these microgrids is DC. Due to developments in power electronics, today it is possible to connect DC and AC by using rectifiers and inverters. Today, microgrids can integrate with renewable energy plants and also with distributed generation plants where the outcome is combined heat and power, which in short is termed CHP—this concept of CHP also came from the Edison’s Pearl Street station, as shown in Figure 2.3 [8].

2.3 NEED FOR MICROGRIDS Today there exist some places with lack of electricity services, mainly rural areas and semi-urban areas of underdeveloped and developing countries. According to the International Energy Agency, nearly 940 million people (13% of the total population) across the world and 240 million people in India do not have access to electricity in 2017. The extension of the central grid in many of these places is the traditional approach, which is technically and financially inefficient due to the existing insufficient energy service along with reduced grid reliability, extended building times and construction challenges in remote areas. Many of the challenges faced by traditional lighting or electrification strategies can be overcome by adequately financed and operated microgrids based on renewable and appropriate resources. The world is expecting universal access to electricity by 2030, but progress towards it is quite slow. To reach the goal of universal accessibility, microgrids play a vital role. Governments, NGOs and other private developers throughout the world are focusing on microgrids to electrify communities that are likely to be served in the near or medium term by extensions of traditional centralized grid systems. As a result, the number of microgrids being developed is increasing rapidly. Although the power rate of microgrids is limited to a few mega-volt ampere (MVA), it is relative to its application area and grid type. To meet higher power demands, there is need of a large number of microgrids, and these are interconnected with increased stability and

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necessary control technologies; this interconnection is referred to as power parks. The involvement of renewable sources makes the microgrid eco-friendly by decreasing environmental emissions. The provision of energy security for local customers or communities is also provided and operated without the involvement of a central grid. The reliability, sustainability and economics are the most important goals for the electrical society, and these three goals can be fulfilled with microgrid technology [9–11]. When compared to conventional grid applications (where only 33.33% of fossil fuel consumed is converted to electricity and the remainder is dissipated as heat energy), microgrid technology can interact with consumers on its distribution side, and therefore it can manage the power demand and supply. Not all the power generated in the main grid is utilized completely by consumers because about 5–7% of power is lost during transmission in the transmission lines. Coming to a microgrid, all the power generated stays at the distribution level, and 20% of microgrid generation capacity helps to meet the 5% peak demand in the utility grid. One of the most important features of microgrids is islanded operation or independent operation during events of widespread failure or power fluctuations (intentional or unintentional) or even for instants of cost optimization. The microgrid enables a black start facility, which might be required during disasters or calamities. Microgrid capacity ranges from one to a few hundred kW depending on the number of customers connected and type of loads attached. The services provided by the microgrid include residential lighting, fans, refrigerators, TVs and so on. Depending on the favorable conditions of the place and with respect to the capital cost, the type of generation resource employed varies. Generation resources could be solar PVs, windmills, biomass gasification, hybrid technologies such as solar PV and diesel or wind diesel. All are familiar with diesel-based microgrids due to the widespread availability of diesel and low upfront capital cost when compared to other microgrids. It is also well known that diesel generators (Figure 2.8) may last for 40 years or more, because the trend of renewable energy sources for backup generation during power outage is becoming successful. Microgrids with renewable energy source–based generation are drawing more interest. Micro-hydro-based microgrids are generally situated near river areas and are much more similar to run-of-the-river– type schemes where a pipe is used to divert the direction of the water from a river or a stream and this water is allowed to flow through a turbine to generate electricity. Biomass gasification systems use biomass that has undergone incomplete combustion to produce syngas, and this gas is burned in an engine to run a generator. A biomassbased microgrid is suitable for areas where an adequate biomass supply is available. Solar PV–based microgrids are globally known and in widespread use due to the reduction in the global market price of PV cells. With modern technologies, the price of solar cells will be reduced even more. PV cells use solar energy and convert it into electric energy, which is stored in batteries and used as backup. Wind-based microgrids are no less popular than solar-based microgrids. Windmills convert wind energy into electric energy, and it is stored in batteries. A battery storage system is required in both solar- and wind-based microgrids for uninterrupted power supply and for backup plans during power outages. There are some prospective features of any electric grid, which include the involvement of independent microgrids with ease of connecting and disconnecting [12, 13].

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The features also include the following:

1. The harnessing of power and heat simultaneously as in combined heat and power technology. 2. Minimization of transmission losses. 3. Response to energy demand. 4. Integration of renewable energy resources. 5. Power quality enhancement and improvement. 6. Future prediction of generation and load. 7. Resilience to grid failures. 8. Increase in number of active consumers in power production and stakeholders in transactions of electric energy. 9. Improvement in energy storage devices and so on.

The mentioned features can be fulfilled by the development of microgrids into the existing utility grid [12].

2.4 DEFINITIONS OF MICROGRIDS Microgrids can be understood by different definitions given by different departments or research, but the ultimate goal is to construct a sustainable and efficient microgrid. Two definitions of microgrids are given in the following. Per the United States Department of Energy’s microgrid exchange group, a microgrid is defined as a group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. A microgrid can connect and disconnect from the grid to enable it to operate in both connected and islanded mode. The European Union research project has a definition of microgrid in its terms, stated as follows: “A microgrid comprises low voltage distribution systems with distributed energy resources, storage devices, energy storage systems and flexible loads and such systems can operate either connected or disconnected from the main grid.” In a short, a microgrid can also be defined as a modern power distribution system where local sustainable energy or power resources are designed with the help of various smart grid initiatives. In all the definitions, the term “distributed energy resources” is used, and it refers to power generation situated near the consumption site or nearby loads. Main power generation in a centralized grid can be referred to as central generation, and power generation situated near the distribution network as a part of a microgrid is referred to as distributed generation.

2.5 STRUCTURE OF MICROGRIDS A microgrid is an interconnection of different renewable distributed generators, nonrenewable distributed generators with devices capable of storing energy and various types of microgrid loads. The structure of a microgrid also includes the interconnection of other microgrids, control and stability systems and communication systems between the consumer and owner. The interconnection between the main grid and

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FIGURE 2.4  General structure of a microgrid.

the microgrid is known as a point of common coupling [1]. The general structure of a microgrid is shown in Figure 2.4, where both renewable and nonrenewable energy sources are distributed energy resources. The construction of a microgrid involves the components of distributed generation, energy storage devices, converter technologies, power management systems and control technologies for stability and efficient operation of the microgrid.

2.5.1 Distributed Generation “Distributed generation” refers to generation of power from nearby consumer regions. It reduces or eliminates the generation cost: distribution cost along with transmission cost when compared with existing main generation units. It also increases the efficiency by the removal of elements that are the reason for the complex and interdependent nature. In most situations, distributed generation may provide lower generation costs with more reliability and enhanced security, which is not achieved by the existing generators. Distributed generation uses small-scale technologies for production of electricity near the end users of power. For an increase in potential benefits, distributed generation technologies include modular generators and sometimes

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renewable energy resources. When compared with traditional power generators, distributed generation can offer lower-cost electricity and higher power reliability and security with fewer environmental consequences, as shown in Figure 2.5. Distributed generation technologies include reciprocating piston engines, gas turbines, fuel cells, wind turbines, PV systems and so on. A  point of common coupling is an important aspect in interconnecting distributed generation with the main utility grid. Distributed generation takes place on two levels, the local level and the end point level, as shown in Figure 2.6. Local-level power generation plants need to be located specifically, and accordingly, renewable resources must be used per the locality. These include solar power generation, small hydro thermal plants and so on. Local-level plants are less centralized and tend to be smaller, which makes them cost and energy efficient with more reliability. The end point level is very similar to the local level, with many common effects, since the individual energy consumer at the end point level applies many of the same technologies stated for the local level. Mostly the modular internal combustion engine is a frequently employed distributed generation by endpoint users. Distributed generation can also operate as an isolated island of electric energy production and serve as a small-scale contributor to the power grid. In the traditional electric power generation approach, a few large-scale generating stations are installed far from load centers, but in contrast, distributed generation systems use numerous but small-capacity plants and can provide onsite power with less reliance on the distribution network and transmission grid. A general layout of distributed generation interconnected with a utility grid is shown in Figure 2.7.

FIGURE 2.5  Schematic illustration of conventional electricity grid with power generation and distribution.

FIGURE 2.6  Schematic illustration of power supply by main grid and distributed generation.

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FIGURE 2.7  Interconnection diagram of distributed generation, utility grid and load by PCC.

The typical uses of distributed generation include domestic purposes such as micro generation of electricity and heat, commercial purposes (building related), greenhouses, industrial purposes, district heating and grid power. Some of main generation technologies which can be used as distributed generation are discussed in the following. 2.5.1.1  Stand-Alone Reciprocating Engine Generators The concept of distributed generation is neither so old nor too new, because with the beginning of electric grid establishment, there were some remote communities, or off-grid communities. For those communities, stand-alone reciprocating engine generators found application as power plants for these communities [14, 15]. Perhaps these units may be the first crude incarnation of distributed generation. These engines run with fossil fuels, which make them relatively inefficient, and these units are also responsible for significant local pollution and need to be replaced by modern technologies or renewable distributed generation. 2.5.1.2 Turbine Generators Turbine generators that are run through gas or gas turbines have been much more familiar in the aerospace industry. In the context of application as distributed generation, these are highly efficient, less expensive and possess mass-produced technology. Turbine generators can achieve a thermal efficiency up to 60% by utilizing the combined generation of electricity from the work of turbine shaft and electricity from the exhausted waste (through a steam cycle). If the waste heat from domestic and commercial heating is utilized to generate electricity, then the efficiency can rise up to 90%. These units can be operated as a medium-sized cogeneration plants. Micro gas turbines (Figure 2.9) can be ideal for small businesses or households, which offers new opportunities for peak shaving and cogeneration at a more localized level.

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FIGURE 2.8  Diesel generator.

FIGURE 2.9  Microturbine.

2.5.1.3 Solar PV Cells Solar PV (Figure 2.10) cells convert solar light energy into electrical energy directly with the help of solar PV modules or solar PV arrays. Solar PV panels are expensive due to their sophisticated manufacturing technology. But with the increase in market demand and modern manufacturing technologies, PV cells are going to become more economically appealing options in the near future. An efficient solar power plant can be seen as a green electricity utility for many consumers, but the capacity factor of solar PV cells makes solar power generation a potentially troublesome generation technology. Use of energy storage devices in these units makes it a more practical option.

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FIGURE 2.10  Solar PV cell.

FIGURE 2.11  Wind turbine.

2.5.1.4 Wind Turbines Apart from solar PV cells, there is an indirect approach to utilizing solar energy, and that approach is converting wind energy, which is an outcome of solar energy, into electrical energy. The use of wind turbines (Figure 2.11) and energy storage devices is relatively expensive, but the outcome is much more beneficial. The utilization of wind power within the infrastructure of distributed generation is limited with respect to geographical regions (with regular and high-quality wind conditions) or to off-grid communities. 2.5.1.5 Fuel Cells Compared to renewable technologies, fuel cell technologies are highly efficient. Here the chemical energy is converted into electrical energy with heat as a byproduct.

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FIGURE 2.12  Fuel cell technology.

The fuel cells generate electricity by means of chemical reactions taking place by transferring charged species across a membrane which separates the fuel and oxidant streams. The fuel used here is hydrogen or methane. The demerit in this technology is the risk factor and the pollutants formed from the side reactions. If those pollutants are avoided, then electric efficiency can be as much as 55% for fuel cells. Recently fuel cells (Figure 2.12) are being explored for use in vehicles and also as a stationary generator. Their use is limited due to the high capital cost.

2.5.2 Energy Storage Technology For successful operation of a microgrid, energy storage technology plays a vital role. The electrical energy available from renewable sources is variable and depends on environmental conditions. So for a smooth supply and in order to legitimize renewable energy sources as a reliable contributor to the main grid, energy storage is important. Continuity in the power supply is possible by balancing power generation with power demand, and without an energy storage process, it is quite difficult [16–19]. The energy storage components present in a microgrid are required to meet the following requirements:

1. The foremost requirement for an energy storage device is to balance power generation at the generation side with power demand on the load side because most distributed generation used in microgrids is intermittent and prone to transient disturbances.

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2. Energy storage devices must be able to store the maximum energy demands during off-peak hours and be able to supply all the consumers or electric loads whenever required. 3. To meet unpredicted and sudden load demands, the energy storage device must be able to eliminate the loaded parts present in the microgrid. 4. It should be able to accommodate the minute-hour peaks that are present in the daily load demand curve. 5. Whenever there is a switching of a microgrid from grid-connected mode to islanded mode or vice versa, the energy storage device has to provide smooth transient conditions. 6. It should be able to ensure sustainability and reliability in terms of constant voltage and frequency operation when the distributed generation is renewable. The classification of electrical energy storage technologies is mainly based on its construction, and they are: 1. Electrical technologies: The electrical storage system includes the super capacitor system and super-conductive magnetic coil. A  suitable storage system in microgrids is a super capacitor, where the electrical energy is stored in the form of a static electric field in the electrolyte between the electrodes and ions. Super capacitors have very high cycle life duration with high power density but with much lower density of energy. That’s why they are used in microgrids where a short-term and high-power storage system is required. They can also be used in hybrid storage techniques with batteries so that their lifetime will be increased [21]. 2. Mechanical technologies: The mechanical storage system includes the pumped hydro-power system, compressed air energy storage system and flywheels. Pumped hydro-energy storage is considered the major storage technology across the world with more than 127 GW installed power. It is used as medium-term storage system due to the range of its energy-to-power ratio from 2 to 8 hours. It consists of two interconnected water reservoirs which are located at different altitudes, for example, a mountain lake and a valley lake, and is connected by using penstocks. This storage technology is used when the distributed generation used is a small hydro-power plant. It is designed for collecting water during the wet seasons so that hydro-power can be produced continuously throughout the year. There is an alternative to hydro-storage systems called a compressed air energy storage system (CAES). To date, only two CAES plants are in operation, one in McIntosch, US, with capacity 110 MW and the other in Huntrof, Germany, with capacity 320 MW. For stabilization functions in weak grids or in distributed generation where renewable sources are used, the mechanical storage system used is a flywheel storage system. The rotating kinetic energy of the flywheel is stored and used during high power demand for short durations with charging discharging cycles [21].

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3. Chemical technologies: A  chemical storage system includes batteries, hydrogen storage and natural gas storage system. The batteries can be classified as lead acid, lithium ion, high-temperature and flow batteries. The major development in battery technology is due to the major application of battery storage systems in microgrids involving renewable energy sources as their distributed generation. Hydrogen storage systems are also chemical energy storage systems, which are expected to be more important for power systems involving high fractions of renewable energy of about 80–100% [21].

2.5.3 Microgrid Loads Although microgrids are constructed for backup purposes and for an uninterrupted power supply, the loads connected to the microgrid should be analyzed properly, and the type of load should be chosen for proper reliability. Various kinds of loads can be connected to a microgrid system, which plays an important role in the operation, stabilization and control of the microgrid. The power system contains different kinds of loads, which are classified as static and dynamic loads. The loads that can be supplied include household and industrial loads, but these loads are critical or sensitive and demand reliability at a high level. The operation of a microgrid requires several considerations with respect to loads. Some of the considerations are priority to critical loads, power quality improvement for specific loads and also enhancement of reliability for pre-specified load categories. In addition to this, distributed generation or local generation also prevents some unexpected disturbances with the help of fast and accurate protection systems [20]. For the prediction of operating strategy in a microgrid arrangement, the following considerations are to be seen before and so that load classification is possible:

1. The load or source operation strategy included in the microgrid is required to match the net active and reactive power in grid-connected mode. 2. The load/source operation strategy included in the microgrid is required to meet the stabilization of the voltage and frequency in islanded mode. 3. The type of load should be a category of power quality improvement type. 4. With respect to the distributed energy resource ratings, there should be a reduction of maximum load for better enhancement.

2.6 OPERATION OF MICROGRIDS The operation of microgrid involves two operations: 1. Grid-connected or grid-tied mode: Per the name of the operation mode, the utility grid or main grid is connected to the microgrid, and the microgrid can demand energy from the main grid, or it can supply energy to the main grid. During this transfer of energy, if there is any disruption occasionally or intentionally, then the microgrid is switched to islanded mode. Since here the main grid is connected to the microgrid, it is termed grid-connected

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mode [25]. There lies a point of common coupling where the microgrid and utility grid are connected, as shown in Figure 2.13. 2. Islanded mode: Per the name of the operation mode, the microgrid gets disconnected from the main grid either naturally or intentionally, and the work of the microgrid is autonomous. The ultimate aim is to supply the amount of energy demanded by local needs during the absence of the main grid and not to rely on the main grid, as shown in Figure 2.14. Once the disruption or the disturbance in the main grid is cleared, then the microgrid will get connected to the main grid and thus return to grid-connected mode [25].

FIGURE 2.13  The point of common coupling connecting microgrid and utility grid.

FIGURE 2.14  Operation of distributed generation in islanded mode of operation.

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The switching of the microgrid from grid-connected to islanded mode or vice versa needs a proper monitoring system and a control strategy, which are discussed in further sections.

2.7 MONITORING OF MICROGRIDS For the suitable operation of a microgrid, a monitoring system is required to match the following tasks and requirements:

1. It is required that the monitoring system use a distributed structure so that it is possible to monitor individual generation units simultaneously, and reliability, along with microgrid system’s operational efficiency, will also improve. By using a distributed structure, it is also possible to monitor the operating parameters comprehensively. 2. The monitoring of the microgrid system must be able to support different varieties of protocols related to the communication system. The support of communication protocols is necessary to ensure communication between the user and terminal device located at the distributed side or distributed generation, which includes the receiving signal parameters (either in analog or digital form) and handling them in different formats. 3. The configuration of the microgrid system should be defined, and the configuration of different subsystems and devices present in the main system should be defined. An online modification facility must be present where the configuration of all types of tasks to be performed is defined. 4. The monitoring of the microgrid also includes the monitoring of the main grid operating system, individual subsystem operation and every device involved in the microgrid system. It enables the remote controlling of the microgrid and monitors the switching modes of the microgrid. 5. Microgrid system state prediction is required for the promotion of operation of the microgrid system and also for its control. So, to make accurate predictions, one must analyze the previous state of the system, which is possible from the stored operation data from time to time. This requires the continuous monitoring of the microgrid system [21–24]. Hence, a monitoring system is required to record and store the long-term operating data of the microgrid. A schematic block diagram of a monitoring system is shown in Figure 2.15, including the use of peripheral component interconnect extensions for instrumentation (PXI), which are PC-based technologies that help in storing a high number of channels with high performance and high speed. Apart from state prediction, the stored operation data can also be used to analyze the causes of accidents, for analysis of statistics related to the operation and economy of microgrid and for future modifications or developments. The equipment present in the subsystem is considered a monitoring device, and distributed generation is considered subunit monitoring [25]. The core of the monitoring system is referred to as the inverter because the grid-tied inverter is known as the heart of all distributed generation units connected, and it stores almost all operating and running data. For detection

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FIGURE 2.15  The use of peripheral component interconnect extensions for instrumentation in monitoring of a microgrid.

of the status of the switch that transits the mode of operation of the microgrid and for remote monitoring, the data recorded from the communication between various devices and distributed generation ports is used. The control system of the microgrid sets different control processes and specific programs for continuous monitoring and operation of the microgrid.

2.8 STABILITY, CONTROL, AND STRATEGIES FOR MICROGRIDS After discussing the monitoring of microgrids, the issues to be focused on most importantly in the context of microgrids are stability issues. Stability issues are more common in microgrids compared to main utility grids because microgrids have much lower energy and power ratings than main grids. Microgrids can be of two types based on the nature of their output power, AC and DC microgrids [26]. The concept of stability issues in AC microgrids and their analysis are much more similar to that of main grid. Active and reactive power controls can regulate the voltage and frequency of the system. The stability can be controlled by torque and speed control of the shaft of the machine only if the distributed generation used is a conventional generator (standalone generator) and is connected directly without a power electronic interface module. In DC microgrids, there seem to be fewer or no stability issues because there are no interactions of reactive power in DC grids. The regulation of system frequency is important in DC microgrids. Apart from the stability issue, the major issue faced by any microgrid is power quality, and this issue is also raised during the interconnection of distributed generations. Based on the regulation of voltage and frequency, stability is classified into two types, voltage stability and frequency stability. The changes in

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system frequency occur due to transients and small signal disturbances, for example, during switching a microgrid from one mode to another, and the changes are to be eliminated, which is termed frequency stability. The stability of a microgrid is generally classified as one of two types. Small signal stability methods, which are analyzed from the basis of closed-loop controllers, can be used to handle the problems of continuous switching loads and manage the demand of power by distributed generations or micro sources. Transient disturbances can be seen during a fault occurring in any one of the subsequent microgrids that are in islanded mode, and this affects the entire microgrid system in the name of transient stability. The voltage stability problem in microgrids can be caused due to transient sources like tap changers or due to changes in load (or load dynamics) [30–33]. Also, by limited reactive power, if there is a mismatch in the specified voltage levels of a microgrid and utility grid, then voltage stability or voltage regulation is required. By using well-developed and suited control strategies with additional closed-loop controllers and observers, small signal stability can be enhanced. By using proper or advanced storage devices and suitable protection devices for the microgrid, transient stability can be improved. To improve the voltage stability of the microgrid, voltage-regulating devices and reactive power compensators can be employed. In addition, current limiters and load controllers can also be employed to ensure of microgrid stability. The strategy of control systems used in microgrids requires the selection or optimization of an operating point for the microgrid either manually or autonomously. For insertion of new distributed generations or micro sources or for removal of old distributed generations from the microgrid without any moderation in the existing components of the system, the operation of a microgrid with proper control strategy is needed. There is a requirement for controlled operation of the microgrid for independent control of active and reactive power, for correction in voltage sags and for meeting the load dynamics that are involved in a utility grid [6,7]. To control the operation of the switch that connects or isolates the microgrid from the utility grid per the demand, control technology for a microgrid is a must because it allows smooth switching during demand or intentional islanding and immediate switching during faults or unintentional islanding. The current developments in the microgrid area are mostly related to different control strategies that are to be employed in microgrids for efficient operation. Different control strategies include independent control of individual distributed generation sources, improvement strategies in the central control system and agent-based observing system strategies. Independent control of individual distributed generation sources mainly refers to the governing control of traditional generators by itself, which improves adjustment to the existing grid system with variable conditions. Improvement in the central control system deals with the increase in infrastructure related to communication strategies and easy integration of the increased infrastructure into the main system. An agent-based observing system implements remote or local control of the microgrid at various levels. Due to this method, the robust nature of centralized and distributed control systems can be exploited. The control systems used in microgrid systems can be classified into three categories depending on the controllers used: localized control systems, centralized

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control systems and decentralized control systems and are briefly discussed in the following. 1. Localized control system: For better performance and efficiency of a microgrid, the localized control system plays a vital role in controlling local distributed generations or micro sources. The controllers involved are referred to as local controllers (LCs), and this operation of this control system can be carried out in the absence of any software or any system, as in Figure 2.16. These controllers have interaction with other controllers, so this allows them to work even in the islanded mode of a microgrid. Sometimes this localized control system is also called a decentralized control system because here all the controllers have independent control operation, as in Figure 2.17. It can call an operation or deny it from the point of distribution with respect to the situation, which is a fast solution for the entire system. Conversely, a complete decentralized or localized control system can have more problems because all the control operation is at the distributed level, which may lead the manager to lose control over all the distributed generations and can affect the complete microgrid system. To overcome this situation, a well-organized control system is required to control or govern all the local controllers, which gave rise to the centralized control system [27–29]. 2. Centralized control system: As the name suggests, the control system is centralized and the controllers involved here are referred to as microgrid

FIGURE 2.16  Different layers of local control operation.

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FIGURE 2.17  Location of local controllers in microgrid.

FIGURE 2.18  Centralized control system architecture.

central controllers (MGCCs). To integrate this system into the main system, the system has to be hierarchic in nature, as shown in Figure 2.18. All the local controllers and distributed generations along with loads are interlinked with the MGCCs and provide control over their operation. A centralized control system can achieve comprehension from a specified central point that usually depends on the type of network, and it could be a controller, a server or a switch. The installation of a centralized control system helps the operator control the entire system to meet the necessary power demands with ease. However, there is a solo control device or single MGCC at a centralized control system that stores all the data, measures it and processes it. This operation of a unique controller device may cause several communication problems in centralized control systems and could cause severe faults, which may lead to shutdown of the entire system [34–36]. 3. Distributed control system: In a distributed control system, the MGCC is absent, but interconnection between the local controllers is present for a

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communication strategy between the LCs. In this control system, LCs can communicate directly with one another and can coordinate or negotiate for optimal control over the microgrid. If there is any single point of failure, then distributed control is the best backup for it. The three control systems at a glance are shown in Figure 2.19. However, the construction of distributed control systems deals with numerous complexities in terms of analytical performances, and communication lags between LCs. The speed of this system depends on the communication speed and operation speed of LCs [34]. 4. Multi-agent-based control system: There exists another concept of control system which involves the properties of both centralized and localized control systems and is known as a multi-agent-based control system (MAS). It is similar to a distributed control system, but it involves the operation of agents which operate depending on the data achieved from LCs and send the control to the respective controllers. As shown in Figure 2.20, there

FIGURE 2.19  Difference between three control systems.

FIGURE 2.20  Multi-agent-based control system.

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is interaction between controllers, with each helping to improve the infrastructure of the communication strategy in a wide area where the voltage stability is taken care of by MAS [35]. Numerous research works and models have proposed control strategies for microgrids to enhance their performance and efficiency.

2.8.1 Microgrid Control Techniques In a microgrid, distributed generations are connected with a bus bar through the power electronic interface module, which is different from central generators that are connected directly to the main grid without any interface. The necessity of energy storage devices in the microgrid was discussed earlier. That’s why conventional methods are not employed in microgrids as in main grids. There exist several control technologies or techniques to be employed in the control systems of microgrids. The techniques can be classified as master and slave control, current and power flow control and peer-to-peer control or droop control [37]. In master and slave control, the circuit operating as master regulates the voltage values and values of frequencies, and the circuit operating as slave controls the current sources present in the system. The current and power flow control method controls the power distribution among the devices and current distribution by employing certain control signals. The peerto-peer control method is completely based on the method of external characteristics of declining. Peer-to-peer control methods manage the conditions of a single point of failure of distributed generation connected in the microgrid. If single-agent failure occurs in the system, then the information and communication won’t also be interrupted in to peer-to-peer connection model. Since it is based on droop characteristics, it can also be termed the droop control method. The droop control method is combined with either the master and slave control method or current and power flow control method to improve their control availability because the system includes power converters which act as non-ideal voltage sources [38–41]. With the help of a specific control algorithm, the system frequency and voltage can be adjusted autonomously without the involvement of communication. Drooping characteristics are seen in f/P (frequency versus active power) control and V/Q (voltage versus reactive power) control where the reference P and Q of distributed generations are produced by measuring the frequency of the microgrid system and the magnitude of output voltage of respective distributed generations. There is an alternative control method, P/f and Q/V, where the reference frequency and magnitude of voltage are produced by measuring the respective P and Q of distributed generations.

2.9  INTERFACE MODULES OF MICROGRIDS The architecture of a microgrid is not complete without studying the importance of interfacing modules used to connect the distributed generation and bus bar of existing grids. These modules used in microgrids, as shown in Figure 2.21, are made of power electronic converters which allow the interconnection of power equipment and components of the common system. Most distributed generations are renewable

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FIGURE 2.21  Microgrid with power electronic interface module.

resources, and they require specialized or advanced power electronic converters to convert the energy generated to a suitable energy form utilized by the consumer or supplied to the utility grid. The use of advanced power electronic converters as interface modules allows the system to meet necessary power demand at better efficiency and at low cost. Since in microgrids different varieties of sources are employed, the use of a power electronic interface will bring stability to the system [41–42]. Distributed energy resources where distributed generations and energy storage devices are used to meet the required power demand involve the conversion of DC energy to AC energy and vice versa. Grid-tied inverters are used to convert the energy generated in the microgrid to a suitable AC form which is compatible with the utility grid. The power electronic interfaces also control the voltage and frequency stability of the microgrid with the grid. In addition, the interface modules have several functions other than conversion of power, such as maintaining power quality, power conditioning of active and reactive power, controlling load dynamics, monitoring and controlling services, protecting output filters and interfaces and so on. The interdisciplinary nature of power electronics helps in changing the voltage characteristics, current characteristics or any characteristics of power into suitable quantities per the power application. Among the power electronic devices used as interfaces, bidirectional converters are assumed to be widely used components in microgrids due to their ability to control power flow. Also, bidirectional converters are able to handle microgrid-generated power in a stable manner either during low or no load operation mode or overload operation mode. The interface takes control of the microgrid when it is disconnected from or reconnected to the main grid. In general, the microgrid operates in grid-connected mode, and if any disturbances occur intentionally or unintentionally, then the microgrid is disconnected from the main grid at the point of common coupling. Until the disturbance is cleared, the microgrid operates in islanded mode [42]. Typically, the rated peak power capability of a microgrid is limited to 10 MVA, so microgrids are generally connected at distribution levels with limited handling capacity of energy due to usage of renewable energy resources and their wastage of heat. Hence the relay used

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for the interconnection of the microgrid that interfaces the microgrid with public utilities plays a vital role, and it determines the success rate of microgrid management.

2.10  TYPES OF MICROGRIDS The types of microgrids based on the locality and number of communities are discussed in the following: 1. Customer microgrids: The general structure which is discussed in this chapter is a perfect example of a customer microgrid. These are often called true microgrids. Usually customer microgrids are self-governing and situated downstream of a single PCC. One can imagine this type of microgrid easily because the regulations and structure involved here can neatly fit into our present technology. The restrictions on the nature of the microgrid are comparatively neglected or loose. 2. Utility or community microgrids: Community microgrids are a small section of regulated grids. They are also known as milligrids. Due to the presence of traditional incorporate infrastructure, community grids are different from a statutory and business model view fundamentally, but technically these are no different from microgrids. The inference here is that the regulation of the utility is much more significant and the microgrid has to comply with utility codes that exist, or, if possible, accommodation has to be made to that code. 3. Virtual microgrids: This is the type of microgrid which covers distributed energy resources at numerous sites, but all are coordinated with each other in such a manner that the grid sees them as a single controlled entity. To date, very few demonstrations of virtual grids exist and more development and control techniques are required. This type of system must be able to operate in a controlled islanded mode of operation or in coordinated multiple islands. 4. Remote power systems: The concepts and structure of remote power systems are fairly close to the concepts that are found in microgrid technology; that’s why remote power systems are commonly defined as microgrids. These can also be termed remote microgrids, and these are not operated in grid-connected mode. These power systems work in islanded mode only. These are located in highly dispersed consumption regions. Per A&S Utility distribution microgrids (as of 2012), microgrid categories can be of three different types other than the ones mentioned previously, as follows: 1. Campus environment or institutional microgrids: The main focus of institutional microgrids is the accumulation of existing onsite generation with more than one load located near the same geography so that the manager can easily control and manage the microgrid. 2. Military base microgrids: Military facilities demand a continuous and uninterrupted power supply for physical and cyber security. So, to fulfill this function without depending on a utility grid, microgrids deployed near military base camps are referred to as military base microgrids.

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3. Commercial and industrial microgrids: There are many industrial and manufacturing processes where an interruption of the power supply for a short duration can cause high economic losses to the industries. So industries build microgrids per their demand with necessary standards to avoid interruption in the power supply, and those microgrids are called commercial or industrial microgrids [43–46].

2.11 PROS AND CONS OF MICROGRIDS Microgrids also have two sides, one side with numerous benefits or advantages and the other with disadvantages. Some of the pros of microgrids as identified by Lawrence Berkeley National Laboratory (LBNL) are discussed in the following: 1. Autonomy: A  microgrid allows the autonomous operation of generation devices, storage and loads seamlessly, and with the help of recent advances in technology, microgrids balance out voltage and frequency stability issues. 2. Stability: Irrespective of the main grid in the on or off state, the entire micro-system operates in a stable manner due to the utilization of control techniques that are based on droop characteristics of frequency and voltage levels at the terminal of each device. 3. Compatibility: Microgrids operate as functional units to existing grids, which give assistance in building the existing system and thus helping it to maximize with standard utility assets. 4. Flexibility: Microgrids are flexible, with easier concepts of electricity generation, and can use hybrid technologies by combining more than one type of distributed generation. 5. Scalability: Microgrids provide the facility of distributed generations, energy storage devices, and load devices in a parallel and modular manner, which makes it easy to scale up with higher-power generation and/or consumption levels. 6. Efficiency: Energy management goals—including economic and environmental—can be optimized in a systematic fashion. Blackouts and power interruptions are minimized 7. Economics: The improved reliability of the microgrid significantly reduces the cost experienced by consumers due to power outages and poor power quality. It also leads to active participation of consumers, which builds up economic efficiency. 8. Peer-to-Peer Model: Microgrids represent a new paradigm—a true peerto-peer energy delivery model that does not dictate size, scale, peer numbers or growth rates. Some of the cons of microgrids are discussed in the following: 1. The controlling and consideration of three main parameters: frequency, voltage and power quality, such that they meet with accepted standards, but at the same time, the power and energy balance are to be maintained.

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2. The requirement of more space and maintenance due to storage devices like battery banks or flywheels. 3. If the number of distributed generation options is expanded, then the microgrid becomes more complicated to study and analyze. 4. Regulatory issues are more difficult to comply with by the public. 5. Proper care must be taken when a microgrid is resynchronized with the utility grid. 6. The protection system of microgrids is a major challenge for the installation of microgrids [45, 46].

2.12  MICROGRID ARCHITECTURE Based on the microgrid design concept, the architectures available in microgrids are of three types: 1. AC microgrid 2. DC microgrid 3. AC-DC or hybrid microgrid The three types of architectures are discussed briefly. An AC microgrid is the main form of a microgrid, and it is similar to a utility grid in terms of operation and stability issues. The basic structure of an AC microgrid (Figure 2.22) is the radial type. Depending upon the nature of the load type, the application of the microgrid and the capacity range, AC microgrids are categorized into three types, system-level, commercial or industrial and rural microgrids. There is also a need for DC microgrids because the utilization of DC loads has increased in recent years, and there is development in urban distribution grids. As

FIGURE 2.22  AC microgrid architecture.

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discussed earlier, DC microgrids do not have the problem of synchronization of voltage, as in AC microgrids. This has led to development of DC microgrids at a faster rate. Depending on the type of requirements for electricity, DC microgrids have different architectures, like radial type, ring distribution type and feeder type. The use of power electronic interfaces and bidirectional converters aid the access of AC/ DC loads to DC microgrids. Each structure has its own advantages and disadvantages; for example, the ring-type structure provides the advantage of high reliability in power supply; meanwhile, it has a disadvantage of fault detection and hence the protection system is relatively hard to design. In the radial type, different loads of different levels of voltage are connected to a small voltage-rated DC microgrid through a DC transformer. The advantage of the radial type is reliability of fault detection, and hence the design of protection systems is comparatively easy. Meanwhile, the disadvantage is the low reliability in supplying power. The best architecture for rural places and remote regions is the feeder structure DC microgrid. Compared to the ring-type structure (Figure 2.23), in a feeder structure, a single bidirectional converter is used to connect the microgrid with the distribution system. The advantages of the feeder structure are the simple construction, and the control system is simpler compared to ring and radial types. Also, fault detection and design of protection systems are simpler compared to ring and radial types (Figure 2.24). Its disadvantage is the poor reliability of the power supply. Apart from DC load, there are many inductive loads, and in order to supply all the loads with a single microgrid architecture, one has to use an AC-DC microgrid structure (Figure 2.25). The distributed generation and AC or DC loads are accessed by the DC microgrid with the help of a power electronic interface module, and then the AC microgrid gives access to large systems with the help of transformers. The reliability

FIGURE 2.23  Ring-type DC microgrid.

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FIGURE 2.24  Radial-type DC microgrid.

FIGURE 2.25  AC-DC microgrid architecture.

of the power supply, economy, and flexibility and performance requirements can be achieved by an AC-DC microgrid. Since there are large number of inductive loads internally in the architecture of AC-DC microgrids, such as induction motors and transformers, and there are many power electronic devices which introduce

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harmonics to the system, a suitable static or dynamic reactive power compensation device should be used in the architecture [42–46].

2.13 COMMERCIAL PLANNING OF MICROGRIDS Microgrids are essential and have numerous benefits in terms of society, but in considering the microgrid as a commercial product, there are many issues to be worked on. From a political perspective, microgrids may not seem useful for local communities because they do not see any sort of benefit in replacing a macrogrid with multiple microgrids. Microgrids are not primary agents for the supply of power, and it might take more time to accept them as primary agents. Utility companies that own the manufacture of transmission wires and transmission components see microgrid entry as competition for their business, so they invest more in improving the reliability of macrogrids rather than supporting microgrids. Even the integration of microgrids brings a change to existing grid utility codes, which is commercially not feasible. At present, utility companies disagree about accepting and supporting new technology, but unless they release ownership/control of equipment, microgrids will not be available commercially. The entry microgrids brings an industrial revolution, and utility companies might see a drop in their economical status. So, a lot of research is required to make the microgrid a commercial product [47, 48]. A lot of support and resolving methods are required in the context of commercial planning of microgrids.

2.14 CONCLUSION Research work on the architecture, performance and control methods of microgrid systems is growing more popular with time. Recent developments have proven to establish efficient microgrid systems. In this chapter, the necessity of microgrids and their evolution are discussed so that new ideas and concepts may be developed. The general architecture of microgrids is discussed in brief with every component involved in them. The best alternative for good power quality and uninterruptible power services is the microgrid system. To meet the aim of electrifying each and every house in India, as well as in the world, the development of microgrid systems is a must. It allows access to electricity in remote areas and also encourages the use of non-pollutant energy resources with the help of energy storage technology and other control techniques. The control aspects should be given more importance for a reliable power supply, and also the use of renewable resources as distributed generations creates a chance for sustainable microgrid systems. Stability issues are also discussed in brief, but more research is left to be done on stability issues. Finally, the possible features of future microgrids as a commercial product are discussed, along with political aspects.

REFERENCES 1. E. Hossain, E. Kabalci, R. Bayindir, and R. Perez, “A  Comprehensive Study on Microgrid Technology”, International Journal of Renewable Energy Research, vol. 4, no. 4, December 2014.

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2. Yanbo Che, and J. Chen, “Research on Design and Control of Microgrid System”, in School of Electrical Engineering and Automation, Tinajin University, Przegląd Elektrotechniczny (Electrical Review), ISSN 0033–2097, R. 88 NR 5b/2012. 3. S. S. Sarita, and S Jayalakshmi, “Hybrid Micro Grid Architectures and Challenges”, International Journal for Modern Trends in Science and Technology, vol. 2, no. 9, ISSN: 2455–3778, September 2016. 4. H. Karzazi, and R. Lghoul, “Designing a Smart Microgrid”, in School of Science and Engineering, EGR 4402–01, 21 December 2017. 5. D. P. Kothari, and I. J. Nagrath, Power System Engineering, 3e. New Delhi: McGrawHill, 2019. 6. E. Hossain, E. Kabalci, R. Bayindir, and R. Perez, “Microgrid Testbeds Around the World: State of Art”, Energy Conversion and Management, vol. 86, pp.  132–153, October 2014. 7. “Microgrid Deployment Tracker 4Q12”, Pike Research Report, 2012. Sadrul Ula, T. S. Kalkur, Melissa S. Mattmuller, Robert J. Hofinger, Ashoka K. S. Bhat, Badrul H. Chowdhury, Jerry C. Whitaker, and Isidor Buchmann, “The Electronics Handbook”, Second Edition, April 2005, pp. 1033–1257. 8. Lubna Mariam, Malabika Basu, and Michael F. Conlon, “A Review of Existing Microgrid Architectures”, Journal of Engineering, pp. 1–8, 2013. doi:10.1155/2013/937614. 9. Changhee Cho, Jin-Hong Jeon, Jong-Yul Kim, Soonman Kwon, Kyongyop Park, and Sungshin Kim, “Active Synchronizing Control of a Microgrid”, IEEE Transactions on Power Electronics, vol. 26, no. 12, pp. 3707–3719, December 2011. 10. R. Lasseter, B. Schenkman, J. Stevens, D. Klapp, H. Volkommer, E. Linton, H. Hurtado, and J. Roy, “Overview of the CERTS Microgrid Laboratory Test Bed”, PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, pp. 1, 1, 29–31 July 2009. 11. N. W. A. Lidula, and A. D. Rajapakse, “Microgrids Research: A Review of Experimental Microgrids and Test Systems”, Renewable and Sustainable Energy Reviews, vol. 15, no. 1, pp. 186–202, January 2011. 12. Taha Selim Ustun, Cagil Ozansoy, and Aladin Zayegh, “Recent Developments in Microgrids and Example Cases Around the World—A Review”, Renewable and Sustainable Energy Reviews, vol. 15, no. 8, pp. 4030–4041, October 2011. 13. Ritwik Majumder, “Some Aspects of Stability in Microgrids”, IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3243–3252, August 2013. 14. T. Logenthiran, D. Srinivasan, A. M. Khambadkone, and T. Sundar Raj, “Optimal Sizing of an Islanded Microgrid Using Evolution Strategy”, in IEEE Conference, 2010, pp. 12–17. 15. S. M. M. Tafreshi, H. A. Zamani, M. Baghdadi, and H. Vahedi, “Optimal Unit Sizing of Distributed Energy Resources in Microgrid Using Genetic Algorithm”, in IEEE Conference, 2010, pp. 836–841. 16. F. Tooryan, S. M. Moghaddas-Tafreshi, S. M. Bathaee, and H. Hassanzadehfard, “Assessing the Reliable Size of Distributed Energy Resources in Islanded Microgrid Considering Uncertainty”, in WREC, 2011, pp. 2969–2976. 17. Joydeep Mitra, Shahsi B. Patra, M. R. Vallem, and S. R. Ranade, “Optimization of Generation and Distribution Expansion in Microgrid Architectures”, in WSEAS Conf. on Power System, 2006. 18. L. Tao, C. Schwaegerl, S. Narayanan, and J. H. Zhang, “From Laboratory Microgrid to Real Markets—Challenges and Opportunities”, in 8th International Conference on Power Electronics—ECCE Asia, 2011, pp. 264–271. 19. Jinwei Li, Jianhui Su, Xiangzhen Yang, and Tao Zhao, “Study on Microgrid Operation Control and Black Start”, Power System Conference, pp. 1652–1655, 2006.

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20. A. Salam, A. Mohamed, and M. A. Hannan, “Technical Challenges on Microgrid”, ARPN Journal of Engineering and Applied Sciences, vol. 3, pp. 64–69, December 2008. 21. W. Jian, L. Xing-yuan, and Q. Xiao-yan, “Power System Research on Distributed Generation Penetration”, Automation of Electric Power Systems, vol. 29, no. 24, pp. 90–97, 2005. 22. A. M. Azmy, and I. Erlich, “Impact of Distributed Generation on the Stability of Electrical Power System”, Power Engineering Society General Meeting, vol. 2, pp. 1056–1063, 2005. 23. J. G. Slootweg, and W. L. Kling, “Impacts of Distributed Generation on Power System Transient Stability”, Power Engineering Society Summer Meeting, vol. 2, pp. 862–867, 2002. 24. R. Lasseter, A. Akhil, C. Marnay, and J. Stephens, et al., “White Paper on Integration of Distributed Energy Resources. The CERTS Microgrid Concept”, in Consortium for Electric Reliability Technology Solutions (CERTS), CA, Tech. Rep. LBNL-50 829, 2002. 25. R. H. Lasseter, and P. Piagi, “Control and Design of Microgrid Components, Final Project Report”, in PSERC Publication 06–03. 26. Toshihisa Funabashi, and Ryuichi Yokoyama, “Microgrid Field Test Experiences in Japan”, Power Engineering Society General Meeting, pp. 1–2, 2006. 27. S. Morozumi, “Micro-Grid Demonstration Projects in Japan”, in IEEE Power Conversion Conference, April 2007, pp. 635–642. 28. Oleg Osika, Aris Dimeas, and Mike Barnes et al, “DH1_Description of the Laboratory Micro Grids”, in Tech. Rep. Deliverable DH1, 2005. 29. J. Chen, Y. B. Che, and J. J. Zhang, “Optimal Configuration and Analysis of Isolated Renewable Power Systems”, in 2011 4th International Conference on Power Electronics Systems and Applications (PESA), 2011, pp. 1284–1292. 30. Eftichios Koutroulis, Dionissia Kolokotsa, Antonis Potirakis, and Kostas Kalaitzakis, “Methodology for Optimal Sizing of Standalone PV—Wind”, Solar Energy, no. 80, pp. 1072–1088, 2006. 31. S. M. Shaahid, and M. A. Elhadidy, “Economic Analysis of Hybrid PV diesel-Battery Power Systems for Residential Loads in Hot Regions”, Renewable and Sustainable Energy Reviews, no. 12, pp. 488–503, 2008. 32. J. D. Ren, Y. B. Che, and L. H. Zhao, “Discussion on Monitoring Scheme of Distributed Generation and Micro-Grid System”, in 2011 4th International Conference on Power Electronics Systems and Applications (PESA), 2011, pp. 1–6. 33. Y. B. Che, and J. Chen, “Control Research on Microgrid Systems Based on Distributed Generation”, Applied Mechanics and Materials, vol. 58–60, pp. 417–422, 2011. 34. R. O’Gorman, and M. A. Redfern, “Enhanced Autonomous Control of Distributed Generation to Provide Local Voltage Control”, in Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE, July 2008, pp. 1–8. 35. J. P. Lopes, N. Hatziargyriou, J. Mutale, P. Djapic, and N. Jenkins, “Integrating Distributed Generation into Electric Power Systems: A Review of Drivers, Challenges and Opportunities”, Electric Power Systems Research, vol. 77, no. 9, pp. 1189–1203, 2007. 36. R. H. Lasseter, and P. Paigi, “Microgrid: A Conceptual Solution”, in Power Electronics Specialists Conference, PESC, June 2004, pp. 4285–4290. 37. N. Hatziargyriou, “Microgrid Control Issues”, in Microgrids: Architectures and Control, 1st ed. New York: John Wiley & Sons, 2014, ch. 3, pp. 25–80. 38. T. Xu, and P. Taylor, “Voltage Control Techniques for Electrical Distribution Networks Including Distributed Generation”, in The International Federation of Automatic Control, 17th World Congress Seoul, 2008.

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39. A. Erinmez, D. O. Bickers, G. F. Wood, and W. W. Hung, “NGC Experience with Frequency Control in England and Wales-Provision of Frequency Response by Generators”, in Power Engineering Society 1999 Winter Meeting, IEEE, vol. 1, January 1999, pp. 590–596. 40. R. H. Lasseter, “Smart Distribution: Coupled Microgrids”, Proceedings of the IEEE, vol. 99, no. 6, pp. 1074–1082, June 2011. 41. A. Bidram, and A. Davoudi, “Hierarchical Structure of Microgrids Control System”, IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1963–1976, December 2012. 42. D. E. Olivares, A. Mehrizi-Sani, A. H. Etemadi, C. A. Caizares, R. Iravani, M. Kazerani, A. H. Hajimiragha, O. Gomis-Bellmunt, M. Saeedifard, R. Palma-Behnke, G. A. Jimnez-Estvez, and N. D. Hatziargyriou, “Trends in Microgrid Control”, IEEE Transactions on Smart Grid, vol. 5, no. 4, pp. 1905–1919, July 2014. 43. M. Chenine, E. Karam, and L. Nordstrom, “Modeling and Simulation of Wide Area Monitoring and Control Systems in IP-Based Networks”, in 2009 IEEE Power Energy Society General Meeting, July 2009, pp. 1–8. 44. F. Katiraei, R. Iravani, N. Hatziargyriou, and A. Dimeas, “Microgrids Management”, IEEE Power & Energy Magazine, vol. 6, pp. 54–65, 2008. 45. A. Vaccaro, M. Popov, D. Villacci, and V. V. Terzija, “An Integrated Framework for Smart Microgrids Modeling, Monitoring, Control, Communication, and Verification”, Proceedings IEEE, vol. 99, pp. 119–132, 2011. 46. Q. Chen, H. Ghenniwa, and W. Shen, “Web-Services Infrastructure for Information Integration in Power Systems”, in Proceedings of the IEEE Power Engineering Society General Meeting, Montreal, QC, Canada, 18–22 June 2006. 47. G. H. Yang, and V. O. Li, “Energy Management System and Pervasive Service-Oriented Networks”, in Proceedings of the First IEEE International Conference on Smart Gird Communications, Gaithersburg, MD, USA, 4–6 October 2010. 48. A. Mercurio, A. Di Giorgio, and P. Cioci, “Open-Source Implementation of Monitoring and Controlling Services for EMS/SCADA Systems by Means of Web Services”, IEEE Transactions on Power Delivery, vol. 24, pp. 1148–1153, 2009.

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Renewable Energy Source Design in Microgrids Kumari Namrata, Sriparna Das, R.P. Saini, and Ch Sekhar

CONTENTS 3.1 Introduction..................................................................................................... 54 3.2 Various Components of Wind Turbines.......................................................... 54 3.3 Different Modes of WECS Operation............................................................. 56 3.4 Classification of Wind Turbines...................................................................... 58 3.4.1 Fixed-Speed Wind Turbines................................................................ 58 3.4.2 Variable-Speed Turbines..................................................................... 59 3.4.3 Variable-Speed Turbines with Partial-Scale Converters..................... 59 3.4.4 Variable-Speed Turbines with Full-Scale Converters.........................60 3.5 Methodology.................................................................................................... 61 3.5.1 Designing a Boost Converter Unit....................................................... 63 3.5.2 MPPT Algorithms...............................................................................66 3.5.2.1 P&O Technique..................................................................... 67 3.5.2.2 Incremental Conductance Technique.................................... 68 3.5.2.3 Fixed Voltage Mechanism.................................................... 69 3.5.2.4 Current Control Mechanism................................................. 70 3.5.2.5 Temperature Control Mechanism......................................... 70 3.5.3 Design of Inverter Controller Unit....................................................... 71 3.6 Interfacing Mechanism.................................................................................... 74 3.7 Change Regarding Power Generation from Solar PV Cells............................ 76 3.8 Controlling Mechanism................................................................................... 76 3.9 Maintenance Devices....................................................................................... 77 3.10 Results and Analysis........................................................................................ 78 3.11 Conclusion....................................................................................................... 78 References................................................................................................................. 79

DOI: 10.1201/9781003121626-353

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3.1 INTRODUCTION The usage of renewable energy (RE) is becoming the ultimate aim to run industry. Generation of power, along with cleanliness of the environment, is now the prime focus of today’s world. The important role of energy in the economic health of any country is shown by the gross national product (GNP). The per capita GNP of any country is correlated to the energy consumption per capita. In the 21st century, the use of renewable energy is one of the most important goals in countries’ advancement. Due to easy availability and continuous reform by natural processes, industries are focusing on the usage of renewable sources of energy such as sun, wind, waves, ocean and so on. The initial installation of these sources is a bit difficult during the starting phase, but once it is done, the consumer can get a continuous supply of power with less required maintenance. In this module, a single-grid system is represented by a small microgrid by attaching distributed generation at the distribution or the load side. With the increase in demand of the electrical load, there is an effect of increase in the transmission capacity of long-distance transmission lines, so the dependency of the interconnection of the grid is required. The concept of a microgrid deals with power production in a smaller area. Basically, small generating units are attached to the grid to form a microgrid that will supply a limited region. The extraction of power is done by setting up small generating stations running with energy from sun, wind and other renewable sources of energy. This will reduce the transmission and distribution losses of the system, improving system stability. In this chapter, the design of renewable energy sources is discussed in detail. Power is extracted from renewable energy sources like wind turbines, solar PV arrays and so on to supply the critical loads connected within the microgrid. Under the presence of a single-grid system, when faults occur, the whole system will have power cut, resulting in a complete shutdown or blackout of the system. In the case of a microgrid connected within a small locality, even if a fault occurs in the system, the microgrid will supply the connected area, preventing shutdown. This is the reason for replacing a single-grid system by dividing it into small interconnected grids. Also, microgrids can store power when a utility grid is present and can supply it back to the utility grid in case of need, for participating in money-earning activities.

3.2 VARIOUS COMPONENTS OF WIND TURBINES The aim of wind turbine system development is to increase the output power continuously. Today, due to increasing demand for load, the generation requirement is also high. So, the generation capacity is certainly increased for wind turbine systems. This increase in energy is to meet line losses and an increase in load demand. By the year 1999, the average output power from new installations increase to 600KW from wind turbines [1]. To meet the increase in demand every year, production has been increased. In Figure 3.1, the increment in power production in the current scenario is clearly shown. Generation from wind turbines is classified into two types, onshore and offshore generation. Offshore wind provides a lot of potential. Generation from wind doubled between 2013 and 2016. Wind energy constitutes 16% of the total energy production of electrical energy. The blowing of the wind has kinetic energy and is converted to

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FIGURE 3.1  Installed capacity of power production in corresponding years.

electrical energy by a wind energy conversion system (WECS). The wind rotates the blades of the turbine, and kinetic energy is transformed to rotational energy, rotating the turbine shaft, and coupled with a generator to produce electrical energy. The power generation depends on the size and structure of the blades of the wind turbine. The power output is directly proportional to the diameter of the rotor and proportional to the cube of wind speed. The major parts of a wind turbine consist of a tower and a nacelle placed on the top of the tower. There are several components of the nacelle, which has a specific function in the energy conversion process from wind to electrical energy. Other main components of a wind turbine include a turbine rotor, gear-box, generator, power electronic devices, control arrangement and transformer, and it is attached to a grid. The kinetic mechanical power from the rotor is transferred to the generator by a transmission system which consists of a rotor shaft, mechanical brakes and gear-box. The mechanical brakes are kept as a backup for sudden stopping of the wind turbine. The main purpose of the gear-box is as a rotational speed increaser. The gear-box converts the slow high torque rotation of the aerodynamic rotor into the faster rotation of the shaft of generator. Considering the geometrical aspects, there are two kinds of gear-box, helical, which consists of gear-wheels with a parallel axis, and the planetary type consisting of epicyclic trains of gear-wheels. For variation of speed and torque, a gear-box is present, making the system bulky. As a result, in modern wind turbine technologies, the generator is designed with a multi-polar structure in order to adapt the rotor speed to the generator speed. The speed of the generator decreases by increasing the pole pairs, and thereby the system becomes lighter. The rotor of the wind turbine captures the power from the wind and converts it into mechanical power [2]. The aerodynamic rotor is made up of a hub and blades, and the blades are attached to the rotor using mechanical joints. The generator acts as an electromechanical component in converting mechanical to electrical power. The generator typically consists of a stator and a rotor. The stator is static, consisting of coils mounted in a certain fashion. The rotor is the rotating

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FIGURE 3.2  Block diagram model of a typical wind turbine.

part of the generator and generates the magnetic field. By the rotation of the rotor, the magnetic field passes through the stator windings and voltage is induced in the terminals of the stator. Either a permanent magnet or electromagnet can be used in the rotor. When the magnetic field of the stator follows the magnetic field of the rotor, the generator is termed a synchronous generator; otherwise, it is called an asynchronous generator. A synchronous generator can be used or replaced by an induction generator, which is an asynchronous-type generator. In Figure 3.2, a model of a wind turbine is seen. The diagram depicts from the rotation of the blades of the turbine to the end connection with the grid. The presence of the transformer is to step up the voltage, as the voltage generated by the turbine is lower.

3.3 DIFFERENT MODES OF WECS OPERATION A synchronous generator (SG) operates at synchronous speed. It is dictated by the frequency of the connected grid, regardless of the torque applied [3]. The magnetic field in the synchronous generator can be created by using permanent magnets or conventional field windings. The speed at which the generator rotates is dependent on the frequency of the rotating field and the number of pole pairs of the rotor. It is much more expensive than an asynchronous generator. However, one significant advantage in comparison to an asynchronous generator is that it does not need a reactive magnetizing current, and thus no further compensation is required. There are two types of synchronous generators:

1. Wound round synchronous generator 2. Permanent magnet synchronous generator

Wound round synchronous generator (WRSG): The WRSG is considered the workhorse of the electrical power industry. Its stator windings are attached to the power grid; hence, the speed of rotation is fixed, as the frequency of the supply grid remains fixed. DC current flows through the rotor winding to generate the exciter field, which rotates with synchronous speed.

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Permanent magnet synchronous generator: Here the stator used is of the wound type, and a permanent magnet is present along with the rotor. As there is an absence of an energy supply, its efficiency is high. But the cost of producing this type of magnet is high, and it is also a bit difficult. If permanent magnets are used, then converters should be present to take care of voltage and frequency. Asynchronous generator: There are various advantages to using an asynchronous generator. This includes the simplicity of their working and formation, robustness and low cost. But the main disadvantage is due to the requirement of reactive magnetizing current by the stator. As permanent magnets are not present and separate exciting is not done, for excitation, reactive power consumption is necessary. The reactive power will either be provided from the grid or by introducing new power electronic devices in the system. The electric field is induced by making a relative motion known as a slip between the rotor and stator field, which is rotating in nature. This results in the formation of torque that acts on the rotor. The rotor includes two basic types of design, the squirrel cage and the wound rotor. In the case of a squirrel cage induction generator, there are slots placed on the rotor, and end rings are attached on both ends. It is robust in nature, and the speed varies much less with changes in slip with the speed of the wind. For wind turbines using this type of generator, the presence of soft starting and reactive power compensation equipment is mandatory. The slope of the curve produced by the torque to speed variation of the generator is very high, so any type of change in wind speed directly affects the grid [4]. This creates a major problem when the wind turbine is synchronized with the grid, as incoming current increases up to seven to eight times the rated value. In the case of a weak grid, these currents will cause a very high amount of voltage fluctuation. That is the reason a soft starter is mandatory to limit the current and to connect with the grid. The reactive power that is generated is also dependent upon the wind. When the wind is blowing at high speeds, the wind turbine is able to produce much more active power if and only if the supply of reactive power to the generator is high. If it is low, then the reactive power is drawn from the power grid, and transmission loss will occur and the grid might become unstable. To prevent this situation, converters and capacitor banks are used for reactive power compensation. In the case of the wound round induction generator (WRIG), rotor windings are connected with power electronic equipment or by an arrangement of slip rings and brushes. In this way, the electrical parameters can be controlled from outside. With the help of power electronic devices, power can be taken from the rotor circuit, and magnetizing of the generator can easily be done from the rotor to stator circuit. The main disadvantage is that it is not at all robust and is very expensive. The two types of wound rotor induction generators used in industries are opti slip IG and doubly fed IG. Opti slip IG: These were widely used in the 1990s, where resistance is added to the rotor winding and this resistance is of variable type. This can be controlled optically by the converter placed on the rotor winding. By this arrangement, brushes and slip rings can easily be removed. But the controlling speed is dependent on rotor resistance [5]. Doubly fed IG: The stator winding is connected to a power grid, so the frequency will not change and will remain the same as the grid frequency. The rotor is attached

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to the grid via power converters. The speed range varies according the size of the converters used. Full speed is not required, so converters are designed for 70% of the speed to be utilized. This selection of converters depends upon cost. So, when the speed range increases much more from synchronous speed, the cost increases [6]. It can handle active as well as reactive power components.

3.4 CLASSIFICATION OF WIND TURBINES Wind turbines are classified by how they control the speed. They are as follows:

1. Fixed-speed turbine 2. Variable-speed turbine 3. Variable-speed turbine with converters (partial scale) 4. Variable-speed turbine with modified converters

3.4.1 Fixed-Speed Wind Turbines A fixed-speed turbine (Figure 3.3) uses a gear-box and squirrel cage IG to convert the mechanical form to the electrical form of energy. This concept was also found in many Danish wind turbines [2] operated in the 1980s; therefore, it is referred to as “Danish concept.” Here the transformer is placed before the grid to step up the voltage to the required level. Soft starters are present for synchronizing between the grid and wind turbine, whereas the presence of the capacitor is for reactive power compensation. Since the squirrel cage induction generator operates in a small region with a smaller deviation from synchronous speed, this type of wind turbine runs at a constant speed. It is simple and robust in nature. The cost of the design is relatively low. As it operates in almost constant speed, in the presence of fluctuations in wind speed, there will be a change in mechanical power, effecting a change in electrical power and reducing the system stress [7]. But the major disadvantage is the limitation of the control mechanism to look into the power quality of the system, and reactive power consumption cannot be controlled in this type of wind turbine system.

FIGURE 3.3  Structure of fixed-speed wind turbine.

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3.4.2 Variable-Speed Turbines This configuration involves limited control of the speed of the wind turbine, known as opti slip or flexi slip [5]. There is a wound round induction generator where the stator is directly connected to the power grid. There is variable resistance, connected in series with the rotor winding. By varying the resistance, the speed and energy generated can easily be controlled. Due to the presence of resistance in the configuration, losses occur due to heat. A soft starter is present to control the in-rush current, and a reactive compensator supplies reactive power to the generator. The control arrangements include controlling of variable resistance and pitch [8]. The main advantage is that, without the presence of slip rings, the operating area of the machine can be increased by varying the speed. But there are certain limitations also, such as a speed limitation due to dependency on rotor resistance. Power loss also occurs due to resistance in the form of heat and poor control of active and reactive power. Figure 3.4 depicts the model of this type of turbine.

3.4.3 Variable-Speed Turbines with Partial-Scale Converters This type of configuration resembles a variable-speed wind turbine using DFIGUREbased generation. The stator of the WRIG is connected to the power grid directly, and the rotor of the WRIG is connected to partial-scale back-to-back converters via slip rings. The speed varies over a wide range. A partial scale signifies 30% of the power generated. The rating of the converters determines 30% deviation from the synchronous speed. The turbine decouples the mechanical and electrical system frequency and performs variable-speed operation. The reactive power is compensated, and there is synchronization between the grid and the wind turbine. There is no requirement for a soft starter. The slip is placed to transfer rotor power with the help of a partial scale converter. The converter controls the rotor speed by controlling its frequency and allowing variable-speed operation. It also controls active and reactive power. The control of the turbine blade by pitch control and converter by electrical control resemble the control arrangements. It is a fairly expensive method, but in previous methods, the rotor energy is wasted through the use of resistance. However, in this mechanism, this energy can be fed to the grid with power electronic devices. It can

FIGURE 3.4  Model of variable-speed wind turbine.

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FIGURE 3.5  Model of partial scale converter-based wind turbine.

operate over a much larger area due to huge variations of speed, while the size of the converter is dependent on the variation in speed, and losses in the converter are also much less. The main disadvantage is that converter and slip rings are present, which require maintenance, and occurrence of faults in the grid due to non-maintenance may lead to failure of the machine. In Figure 3.5, an overview of a converter-based wind turbine is shown.

3.4.4 Variable-Speed Turbines with Full-Scale Converters In this case, full-scale converters are used where the speed range can vary up to 100%. All other advantages are discussed in the case of a partial scale converter using a wind turbine. This can be implemented by using an electrically excited DC generator (WRSG or WRIG) or permanent magnet synchronous generator (PMSG). It doesn’t have a gear box; multiple pole generators are used instead. This will have low speed, as the generator is connected to the hub of the rotor directly. But higher torque is required, so the number of generator poles can be increased to meet the demand. This has a huge advantage because of high efficiency, no slip rings and no gear-box so the system becomes less bulky, has better speed range support by the grid, is low cost and reliability also increases. In Figure 3.6, a full-scale converterbased wind turbine model is shown. In Figure 3.7, there is clear evidence that by varying the wind speed, the power production from the turbine is varied. When the wind speed is low, the extraction of power from the wind by the blades of the turbine is low; hence, there is less production of energy. With increasing wind speed, energy production increases up to a certain limit. The limit is known as the Betz limit, up to which the energy-producing process can be performed. If we increase past the Betz limit, then the wind speed forces the turbine blade to stop rotating, and shutdown of the generation process will occur. This is clearly shown in the figure. The red line is the power generation line, that is, how much power is produced with changing wind speed, and after rising to a certain limit, it becomes constant. That point is the maximum capacity of the wind turbine.

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FIGURE 3.6  Full-scale converter-based wind turbine model.

FIGURE 3.7  Effect of changing wind speed on output of the industry [9].

3.5 METHODOLOGY A simple wind turbine model is taken whose inputs are generator speed, pitch angle and wind speed. The wind turbine rotates for production of energy so the output is torque and mechanical power, as shown in Figure 3.8. This torque helps to run the permanent magnet synchronous generator. The use of the permanent magnet synchronous generator is implemented for certain conditions as follows: 1. Using DFIGURE-based generation is becoming old fashioned nowadays. To control high speed, a gear box is needed, which makes the system more costly. PSMG overcomes this, as it is related to the low-speed process, so a gear box is not required, making the system light, minimizing the cost and also improving the efficiency of the system. 2. Due to the presence of permanent magnets, there is no need for electrical energy supplied from external sources. Therefore, no copper loss will take place within the exciter. There are also no mechanical arrangements such as brushes and commutators, so mechanical losses will not take place, making the system more compact than earlier. 3. In rotor winding, there is no high-powered magnet, making the size small. 4. As the rotor circuit is not present, no current will flow through it; therefore, losses are minimized, as no heat is produced in the rotor circuit. Heat is

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FIGURE 3.8  Simulink model of WECS system [9].

produced only in stator winding heat, and, as it is static and the outermost part of the machine, heat can easily be released through the air, and noise production is also less. For these advantages, the use of PMSG-based generation is essential compared to other models [10]. When the generator is made to run by supplying torque, it helps in the formation of three-phase voltage at the output terminal. Here voltage and current are measured by using a measurement block in MATLAB, and these three phases are converted to a single-phase supply by using a rectifier unit or universal bridge in MATLAB. To step up the voltage to the required, a level single-phase output of the rectifier unit acts as an input to the boost converter. By using this method, the generation is done. Only certain parameters are known, including supply frequency, which will remain constant throughout the process, and the input voltage magnitude. By considering the output current per requirements, the design of resistance, inductance and capacitance, which are the parameters of the converter units, are designed. There might be ripples at the output voltage and current waveforms, so the capacitor is added before taking the output to remove the ripples and lessen the total harmonic distortion, which improves the power quality. So, the function of the capacitor is to remove all the distortions and ripples that are present in the output voltage and current waveforms. Here the nature is single phase, but as the required loads are three-phase in nature for being beneficial, conversion from single to threephase is done by introducing an inverter block and designing an inverter controller unit. After the running of the PMSG, the mechanical speed, which acts as an output of this block, is fed back as an input to the turbine block by adding a gain in its path to continuously run the process. This model acts as an example of closed-loop feedback system, as the output of the generator enters as input to the turbine. The generator, after getting into motion, controls the speed of the wind turbine. The entire simulation is done step-by-step, and after performing each block separately

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and simulation is done, they are attached to get the final result of the wind turbine. In the following simulation, there are three blocks: the wind turbine block, which is an inbuilt block and no changes are made in it; the block that contains the part starting from input, taking to the PMSG to the output of the boost converter; and last is the inverter block, which takes the input from second block and generates three-phase voltage. These three blocks denote three sub-systems, and finally they are linked together to run the whole simulation [11]. In Figure 3.8, there is a clear view of the entire model from outside.

3.5.1 Designing a Boost Converter Unit To increase the voltage to the desired level, a boost converter is attached into the system. The generation capacity is always less compared to the demand. So, the design of the boost converter comes into play. This follows a DC-to-DC conversion scheme. Due to the increase in load and limited generation capacity, this converter is used to meet the demand of the system. The design of the converter deals with setting pulses or the firing angle of the switch. The switch should have the minimum amount of losses. Another alternative to replacing the converter is using cells in series-parallel fashion until the desired output is reached. To design this converter, two parameters are essential, the output or the desired voltage and the load current [12]. The design includes setting the values of the elements such as the inductor, capacitor and resistor that are used in this boost topology. The current and voltage can be considered per requirements. The frequency of the system while designing the whole model is fixed. If the frequency of the module is varied, there will be disturbance introduced into the system and the system will lose synchronism. Considering all the mentioned parameters, the design of this converter section is shown in Figure 3.9.

FIGURE 3.9  Boost converter circuital view.

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The switch with minimum losses that is preferred here is insulated gate bipolar transistor(IGBT). Pulses are generated by comparing a constant value with repeating sequences. The relational operator compares those values and sets the pulses for the switch. The width of pulse considered here is 67%, and no delay is introduced in the system. There is another alternative while designing the pulses. The introduction of a pulse generator will serve the purpose directly. In this case, a non-positive part will come into the scenerio; to eliminate this, adding a costant term is done, as a pulse can never be in a negative sequence. The various formulas [13] that are used for designing the converter are as follows: V0 =

Vin

(3.1)

(1 − D )

Load Resistance =

Duty Cycle = 1 −

V0

Vin V0

Capacitance (C ) = Inductor (L ) =

I0

Vs D fs I 0

I 0D fsVs

(3.2)

(3.3)

(3.4)

(3.5)

Thus, the design of the generated voltage is done in the stated fashion. In Figure 3.10, it is clearly shown how the PMSG [9] is attached with the rectifier unit followed by the boost converter. A new block known as the MATLAB function block can be seen in Figure 3.10. The intention of getting the output constant is prescribed in this block. To get the maximum output, the use of maximum power point tracking (MPPT) is necessary. The code is written in that function box. It generates the pulses for the switch that is the IGBT/diode used in the converter section. Here, a perturb and observe algorithm is written, which is explained subsequently. The output of the increased voltage and current from the boost converter is fed as input to the inverter block, and the waveforms of these parameters of the converter are shown in the following. In Figure 3.11, it is seen that the output voltage increases up to a certain limit, and then after 0.2 seconds, the voltage becomes constant and the value reaches 34V. The boosting is done from 0 to 34 V, which will be fed as input to the inverter. As the voltage remains constant, so it easily shows the MPPT circuit is working accurately; otherwise, there will be fluctuation in the voltage waveform. Next, the current waveform is also necessary, and how it varies is shown in the following. In Figure 3.12, it is observed the current also varies in the same way as the voltage and reaches up to a certain value, then falls a bit and becomes constant. The current increases up to 0.025 seconds, and when it reaches 0.2 seconds, it becomes constant, MPPT is verified [14] and the load attached is resistive, as the current is in phase with

FIGURE 3.10  Connection of the rectifier generator, rectifier and converter units.

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FIGURE 3.11  Voltage variation of the converter section.

FIGURE 3.12  Current variation of the converter section.

the voltage. If the load changes, there will be a phase difference that can be observed between those waveforms. The maximum value of the current is 1.5A; it can be made higher if we design the boost converter for a greater range of values.

3.5.2 MPPT Algorithms The use of this algorithm is to run the system at the highest efficiency so that the maximum amount of power can be extracted from the system. The dealing power of a DC-to-DC converter is quite low, so any loss occurring in the system will cause a decrease in system efficiency. So, the system is to run at MPP such that pulses are generated or maintained in such a way that the system follows the MPP track. There are various mechanisms followed while performing this tracking process. The

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design of this algorithm is associated with the converter on which the tracking is to be done to obtain the maximum amount of power. The various methods of MPPT are categorized as follows:

1. Perturb and observe (P&O) technique 2. Incremental conductance technique 3. Fixed voltage mechanism 4. Current control mechanism 5. Temperature control mechanism

3.5.2.1  P&O Technique The use of this algorithm is the most common. In this mechanism, searching is not dependent on external parameters like atmospheric condition, humidity and so on. The parameters that are necessary for this search are only current and voltage. The tracking of MPP is done by comparing the final and previous sets of values. By comparing these values, pulses are generated for the switches of the converter. When a perturbation is inserted into the system, the on and off periods of the system are affected, so if the nth value is higher than its previous state value, the duty is increased, as the MPP is towards the right side, and if the checking fails, then the duty is decreased. By this mechanism, the tracking process is performed. A  diagrammatic representation of this algorithm [9, 15] is given in Figure 3.13. The MATLAB function used in Figure 3.10 is shown in Figure 3.14 [16].

FIGURE 3.13  Perturb and observe algorithm.

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FIGURE 3.14

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Algorithm used in MATLAB function.

3.5.2.2 Incremental Conductance Technique It is most useful when it is attached with perfection and speeds. Here, only current and voltage parameter are needed [17]. So, from this, power is generated and the derivative of power with respect to voltage, that is,

dP 0 dP 0 , is done. can be written as: dV 0 dV 0

− In0 I dP 0 dI 0 = I0+ = I n 0 + n 0−1 dV 0 dV 0 Vn 0−1 −Vn 0

(3.6)

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There are three conditions while finding the MPPT: 1.

dP 0 is greater than zero, which means the point is on the left side of the dV 0

maximum power place, so the duty must be increased to get the maximum power point location. 2.

dP 0 is less than zero, which means the point is on the right side of the dV 0

maximum power place, so the duty must be decreased to get the maximum power point location. 3.

dP 0 is equal to zero, which is the point where maximum power can be dV 0

extracted, so the duty is kept fixed. By following these three conditions, the point can be identified. This process is costlier than the first one, as it requires two sensors at the same time. It is also far more complex in design aspects, and there are fewer variations taking place at the point of maximum power. Its parameters are also not dependent on external factors such as surrounding, climate and so on. 3.5.2.3 Fixed Voltage Mechanism In this method, the power gets varied with change in voltage due to effect of change in temperature and solar irradiance, keeping one fixed at a time is the prime idea. With increasing in the amount of incoming sun to the PV cell, more power will be produced if the temperature is kept constant, and the voltage also increases slowly, as shown in Figure 3.15. On other hand, if the radiation from the sun is assumed to be constant and the temperature is increased, then the production capacity of power will increase for the PV cell, but it will be seen that with the increase in temperature, there is reduction in voltage. With this mechanism, it is easy to track and works if only a single parameter is available. In Figure 3.16, the effect of the change of power with voltage can be noted with the change in solar rays and temperature [18].

FIGURE 3.15  Output of PV cell when incremental conductance is applied.

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FIGURE 3.16  Effect of change in power with the change in temperature and solar insolation, varying one at a time.

3.5.2.4  Current Control Mechanism In this, an I-V (current versus voltage) graph of the solar cell is required. The current value at which MPP occurs is related to the short circuit current by a constant value Ksc. This constant can operate in a wide range of solar insolation and temperature. Generally, the value of this constant for PV cells is 0.92. But the disadvantage is that the short circuit current must be measured within some interval of time, but its measurement is a bit difficult, as during short circuit, there is no flow of power within the loads. 3.5.2.5  Temperature Control Mechanism In this mechanism (Figure 3.17), the temperature analyzer is replaced by a current analyzer. This model is formulated from the relation of voltage dependent on the amount of sunlight that falls on the PV cell or its temperature [19]. The formula is shown as the following: Vmppt(t) = Vmppt×treference+Umppt(t – treference)(3.7) This shows that the maximum power point voltage at any time t will be equal to the maximum power point at a reference time in addition to the temperature coefficient. There is also a separate formula for the calculation of duty that is given by: ∆(Duty) = [Voutput–Vmpp(t)] ×K(3.8) Using formula of the duty cycle, it is found, and the comparison is done as shown in the flowchart. Here, K resembles the size or the value of how much increment or decrement is done, or the step size, and the use of ∆(Duty) is to find the point of maximum power. This process is very slow, so it is not used as frequently as the P&O mechanism. By these methods, the tracking of that point is done. Because there are fewer parameters needed, tracking can be done efficiently depending upon step size and independently of any external parameters like climate and season. This adds more benefit for users to use the P&O algorithm (Figure 3.18), so the authors have used this method to track the MPPT in the case of power generation from wind turbines.

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FIGURE 3.17  Overview of temperature control mechanism.

FIGURE 3.18  Algorithm of temperature control method.

3.5.3 Design of Inverter Controller Unit The loads attached to the distributor end are not a single-phase supply; they require a three-phase supply, so the design and control of the inverter block are mandatory. In design, the theme is current control, so the voltage source PWM technique is followed. The use of the controller unit is to make a small deviation in the load current and to keep the direct voltage constant. In Figure 3.19, it is seen that two loops have been introduced while designing this model. One will take care of current and the other voltage. The output voltage is compared with the reference voltage, and an error is generated, and via a multiplier it is changed and again fed back to the system [20]. The output of the converter acts as input to the inverter block and is attached to the load, which follows the requirements from the customer.

FIGURE 3.19  Design of inverter controller unit [9].

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FIGURE 3.20  Three-phase output waveform of inverter unit.

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FIGURE 3.21  Wind speed variation with time.

FIGURE 3.22  Variation of tip-speed with change in time.

The design of the inverter module and the three-phase output waveforms of the current and voltage are shown in Figures 3.19 and 3.20, respectively. The wind speed, which is given as an input to the turbine block, is shown in Figure  3.21, the final variation of tip speed with the change of time is shown in Figure  3.22 [9], and the PMSG per phase output waveform is shown in Figure 3.23 [21].

3.6 INTERFACING MECHANISM A generator is used to convert mechanical to electrical energy and is connected to a power grid through power electronic devices. Being placed between the wind turbine and power grid, it should satisfy the requirements of both ends for cost minimization and easier maintenance. On the generator end, maximum power extraction is the main intention due to flowing of the wind and rotation of the turbine blades. On the grid end, the power electronic devices must have synchronization with the grid even when there is a change in wind speed; these converters help maintain the system frequency and output voltage and also maintain reactive power.

FIGURE 3.23  Single-phase output voltage of PMSG.

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3.7 CHANGE REGARDING POWER GENERATION FROM SOLAR PV CELLS To generate power from the sun’s rays, there is a change in the mechanism of the wind. In the case of generation of power from wind, wind turbines are used along with PMSG. The blades of the turbine, on contact of the wind, absorb the energy from the wind and convert it into rotational energy to rotate the shaft of the PMSG, and this generator helps in the generation of the three-phase balanced sinusoidal waveform. But, in the case of solar power generation, there is a replacement of these two blocks with only multiple PV cells to form a PV array. In the case of a standalone system [22], where there is no connection to a utility grid, PV cells are connected to a charge regulator unit, followed by a battery and inverter and finally loads are connected, while in the case of a hybrid system [23], diesel generators and the PV cells produce heat, and that energy is used to run the generator. By proper controlling and handling of power, it is supplied to the loads, forming a hybrid system. The last type is a grid-connected system in which the PV cells absorb energy from the sun, and with proper controlling and handling of power, they are connected directly to the grid. So, in the case of a grid-connected system, the converter and inverter designs are similar to wind turbine power generation, but the main difference is that the turbine and PMSG are replaced by a PV cell. The various types of power production from PV cells are shown in Figures 3.24 and 3.25, where the first one shows a standalone system and the second shows hybrid and grid-connected systems.

3.8 CONTROLLING MECHANISM The extraction of maximum power is the ultimate goal from the wind turbine, so it is associated with control arrangements for making wind turbines operate within a stable region. There are two types of controlling methods:

1. Passive control 2. Active control

FIGURE 3.24  Standalone PV system.

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FIGURE 3.25  (A) Hybrid PV system, (B) grid-attached system.

Passive control uses its own means and takes help from the forces of nature to control the parameters, whereas active control uses electrical, mechanical or other means to control the parameters. When the wind speed is low, the control design should operate in generating optimal power and making the wind turbine run at maximum efficiency, while when the wind speed is high, the wind turbine should be within the limits of producing rated power by reducing the force acting on the blades and loads [24]. Passive or Stall Process: It is a very simple method of power control. The blades of the turbine are fixed to the hub such that the wind hits at a specific angle. So, the rotor is made in such a way that it stalls itself to lower the efficiency when the wind speed reaches the upper limit, that is, the rated limit. Active Process: The other name for this method is the pitch control method. The rotation of blades, either fast or slow, corresponds to high or low generation of power. In the case of active control, the geometry of the rotor needs to be changed, so it’s a bit expensive. The system is designed by using an electronic or computer system controlling the motor. It is controlled by varying the pitch angle, making a small deviation with respect to time when the speed of the wind changes [25]. By this process, only output power is maintained.

3.9 MAINTENANCE DEVICES The devices that are used in maintaining synchronization are as follows:

1. Soft starter 2. Capacitor bank 3. Frequency converters

Soft Starter: helps in limiting in rush currents so disturbance to the system is minimized. Without it, the current becomes seven to eight times the rated value, causing a break in synchronization with the grid and making the system unstable [26]. Capacitor Bank: helps in providing reactive power to asynchronous-type wind turbines that help lessen the reactive power taken from the grid by the asynchronous generator [27].

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Frequency Converters: help in maintaining the frequency and voltage of the generator and connecting various electrical systems with different frequencies. It is also called an adjustable speed drive, in which the rectifier units are present for converting AC supply to direct form. Capacitors used for storage purposes and inverter unit need AC supply because most of the loads are AC in nature, so the inverter converts DC to AC, and the AC current flows through the load and is distributed to the consumers according to their needs [28].

3.10 RESULTS AND ANALYSIS The entire process of how power can be extracted from a wind turbine and phase transformation is shown in this chapter. Each model was separately simulated in MATLAB 2015 a, and the waveforms are shown subsequently. The wind speed varies from 6 to 8m/s, as shown in Figure 3.21, and how the power is extracted depends on the variation with wind speed, shown in Figure  3.7. To obtain the maximum amount of energy, the authors used the P&O mechanism of MPPT and explain the entire process with the flowchart in Figures 3.13 and 3.14. There are certain values considered in running the simulation. The density of air is assumed to be constant, ρ=1.08m3/kg; the value of the gear ratio is 5; the radius is 1.525m; and the speed is 20 revolutions per second, which are the basic parameters in terms of the wind turbine block and control part. Next, comes the design of the permanent magnet synchronous generator in which stator phase resistance is considered to be 1.5 ohms, armature inductance 10mH, flux linkage 0.119Wb, inertia 2J (kg.m2) and the number of poles four. A three-phase round rotor is used. In the case of the design part of the boost converter section, two resistors of 1ohm, C0 =3500 microfarad, C1=500 microfarad and inductance of 200 microhenry are used. The switching frequency is considered to be 5000Hz throughout the system. The block diagram model is shown in Figure 3.8, and the design of the boost converter is depicted in Figure 3.10, followed by its voltage in Figure 3.11 and current in Figure 3.12. The design of the inverter controller to change to three-phase was discussed, and its design is shown in Figure 3.19 and three-phase waveform of current and voltage in Figure 3.20. From the experiment, it is concluded that with an increase in the speed of the wind turbine, the power extraction also increases, but up to a certain limit, and wind turbines should operate only within than limit, known as the Betz limit. The variation of the tip speed ratio can be seen increasing up to 2 seconds and after that attains a constant value. All the values considered while performing this model were disclosed in the case of the wind turbine model, and, further, this chapter discussed how and what replacements need to be done in the case of solar implementation if it is replaced by a wind energy system.

3.11 CONCLUSION In this chapter, it is clearly seen how the design of renewable energy sources [29–30] is taking place. If a fault occurs in a single-grid network, then there will be a complete shutdown, but this can be prevented by breaking a single grid system into microgrids. So, the design of renewable energy sources is very important. This chapter clearly shows each and every model simulated in software, its waveforms and the process

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of how this implementation [31–32] is done along with its waveforms in a practical way. There are many renewable energy sources, but this chapter focuses on solar and wind power generation, which are considered the basic sources of supply. To obtain maximum power, MPPT methods [33] are also briefly explained. A detailed theory of every model along with its waveforms is clearly explained and discussed in this chapter.

REFERENCES 1. “Report on Renewable Energy,” International Renewable Energy Agency (IRENA). [Online]. www.irena.org/wind. 2. S. Muller, M. Deicke, R. W. De Doncker, “Doubly Fed Induction Generator System for Wind Turbines,” IEEE Industry Applications Magazine, vol.8, no-3, pp.  26–33, May 2002. 3. Anca D. Hansen, “Chapter 8: Wind Turbine Technology” in Wind Energy Engineering, Part. 3 Wind Engineering, vol.26, pp. 145–160, 2008. 4. Varin Vongmanee, “Emulator of Wind Turbine Generator Using Dual InverterControlled Squirrel Cage Induction Motor,” in 2009 International Conference on Power Electronics and Drive Systems (PEDS), 2–5th November 2009, pp. 1313–1316. 5. K. Vinoth Kumar, A. Nageswar Rao, M. P. Selvan, “Mitigation of Output Power Fluctuations in Wind Farms with Opti-Slip Induction Generator,” in 2009 IEEE PES/ IAS Conference on Sustainable Alternative Energy, 28–30th September 2009, pp. 1–5. 6. Mohamed Hallak, Mourad Hasni, Mohamed Meenaa, “Modelling and Control of a Doubly Fed Induction Generator Base Wind Turbine System,” in 2018 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 28–31st October 2018, pp. 1–6. 7. Shi Wei, Xing Yan, Yundong Ma, Rixin Lai, “Modelling and Control for a Non-GridConnected Wind Power System Based on a Fixed-Pitch Variable Speed Wind Turbine and a Doubly Salient Generator,” in 2009 World Non-Grid-Connected Wind Power and Energy Conference, 24–26th September 2009, pp. 1–4. 8. Farhan Ullah, Shahzad Ali, Deng Ying, Adnan Saeed, “Linear Active Disturbance Rejection Control Approach Base Pitch Angle Control of Variable Speed Wind Turbine,” in 2019 IEEE 2nd International Conference on Electronics Technology (ICET), 10–13th May 2019, pp. 614–618. 9. Sriparna Das, Om Hari Gupta, “Study and Simulation of PMSG-Based Wind Turbine,” in 2020 EPREC, NIT Jamshedpur, May 2020, pp. 37–46. 10. M. H. Zamani, G. H. Riahy, R. Z. Foroushani, “Introduction of a New Index for Evaluating the Effect of Wind Dynamics on the Power of Variable Speed Wind Turbines,” in 2008 IEEE/PES Transmission and Distribution Conference and Exposition, 21–24th April 2008, pp. 1–6. 11. Hongwei Fang, Dan Wang, “A Novel Design Method of Permanent Magnet Synchronous Generator from Perspective of Permanent Magnet Material Saving,” IEEE Transactions on Energy Conversion, vol.32, no-1, pp. 1–7, March 2017. 12. M. Kesraoui, N. Korichi, A. Belkadi, “Maximum Power Point Tracker of Wind Energy Conversion System,” Renewable Energy, vol.36, no-10, pp. 2655–2662, October 2011. 13. Neeraj Keskar, Gabriel A. Rincon-Mora, “Designing an Accurate and Robust LC-Compliant Asynchronous Boost DC-DC Converter,” in 2007 IEEE International Symposium on Circuits and Systems, 27–30th May 2007, pp. 549–552. 14. Arnab Ghosh, Subrata Banerjee, “Design of Type-III Controller for DC-DC SwitchMode Boost Converter,” in 2014, 6th IEEE Power India International Conference (PIICON), 5–7th December 2014, pp. 1–6.

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15. Sachin Kumar Singh, Ahteshamul Haque, “Performance Evaluation of MPPT Using Boost Converters for Solar Photovoltaic System,” in 2015 Annual IEEE India Conference (INDICON), 17–20th December 2015, pp. 1–6. 16. Saidi Khadidja, Maamoun Mountassar, Bounekhla M’hamed, “Comparative Study of Incremental Conductance and Perturb & Observe MPPT Methods for Photovoltaic System,” in 2017 International Conference on Green Energy Conversion Systems (GECS), 23–25th March 2017, pp. 1–6. 17. Deepak Verma, Savita Nema, R. K. Nema, “Implementation of Perturb and Observe Method of Maximum Power Point Tracking in SIMSCAPE/MATLAB,” in 2017 International Conference on Intelligent Sustainable Systems (ICISS), 7–8th December 2017, pp. 148–152. 18. D. Menniti, A. Burgio, N. Sorrentino, A. Pinnarelli, G. Brusco, “An Incremental Conductance Method with Variable Step Size for MPPT: Design and Implementation,” in 2009 10th International Conference on Electric Power Quality and Utilisation, 15–17th September 2009, pp. 1–5. 19. M. Azzouzi, “Modeling and Simulation of a Photovoltaic Cell Considering Single-Diode Model,” in Recent Advances in Environmental Science and Biomedicine, pp. 175–182. 20. Roberto F. Coelho, Filipe M. Concer, Denizar C. Martins, “A MPPT Approach Based on Temperature Measurements Applied in PV Systems,” in IEEE ICSET, 2010, pp. 1–6. 21. Hong-sheng Chen, Tien-chien Jen, “A  Passive Control Method of HAWT Blade Cyclical Aerodynamic Load Induced by Wind Shear,” in 2017 International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), 3–6th February 2017, pp. 106–109. 22. Yash Ajgaonkar, Mayuri Bhirud, Poornima Rao, “Design of Standalone Solar PV System Using MPPT Controller and Self-Cleaning Dual Axis Tracker,” in 2019 5th International Conference on Advanced Computing  & Communication Systems (ICACCS), 15–16th March 2019, pp. 27–32. 23. Siavash Taghipour Broujeni, Seyed Hamid Fathi, Javad Shokrollahi Moghani, “Hybrid PV/Wind Power System Control for Maximum Power Extraction and Output Voltage Regulation,” in 3rd International Conference on Control, Instrumentation and Automation, 28–30th December 2013, pp. 59–64. 24. K. M. Muttaqi, M. T. Hagh, “A Synchronization Control Technique for Soft-Connection of Doubly-Fed Induction Generator-Based Wind Turbines to the Power Grid,” in 2017 IEEE Industry Applications Society Annual Meeting, 1–5th October 2017, pp. 1–7. 25. Agata Dzionk, Robert Malkowski, “Activity Coordination of Capacitor Banks and Power Transformer Controllers in Order to Reduce Power Losses in the MV Grid,” in 2016 10th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), 29th June–1st July 2016, pp. 27–32. 26. Saroj Kumar Panda, Tusar Kumar Dash, “An Improved Method of Frequency Detection for Grid Synchronization of DG Systems During Grid Abnormalities,” in 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], 20–21st March 2014, pp. 153–157. 27. Jinmok Lee, Jaeho Choi, “Decoupling Voltage Controller Design with Time Response Specifications for Three-Phase DC/AC Inverter,” in 2008 IEEE 2nd International Power and Energy Conference, 1–3rd December 2008, pp. 630–634. 28. Liu Yang, Junrong Peng, Fan Yang, Yinghui Zhang, Hongfei Wu, “Single-Phase High Gain Bidirectional DC/AC Converter Based on High Step-Up Step-Down DC/ DC Converter and Dual Input DC/AC Converter,” in 2019 IEEE10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 3–6th June 2019, pp. 554–559.

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30. 31. 32. 33.

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S. Rajanna, R. P. Saini, “Optimal Modelling of an Integrated Renewable Energy System with Battery Storage for Off Grid Electrification of Remote Rural Area,” in 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 4–6th July 2016, pp. 1–6. S. Rajanna, R. P. Saini, “Optimal Modelling of Solar/Biogas/Biomass Based IRE System for a Remote Area Electrification,” in 2014 6th IEEE Power India International Conference (PIICON), 5–7th December 2014, pp. 1–5. Ajai Gupta, R. P. Saini, M. P. Sharma, “Optimized Application of Hybrid Renewable Energy System in Rural Electrification,” in India International Conference on Power Electronics, 2006, pp. 337–340. S. Rajanna, R. P. Saini, “GA Based Optimal Modeling of Integrated Renewable Energy System for Electrification of Remote Rural Area,” in 2016 IEEE 6th International Conference on Power Systems (ICPS), 4–6th March 2016, pp. 1–6. S. Pant, R. P. Saini, “Comparative Study of MPPT Techniques for Solar Photovoltaic System,” in 2019 International Conference on Electrical, Electronics and Computer Engineering (UPCON), 8–10th November 2019, pp. 1–6.

4

Microgrid Power System Control Designs Renuka Loka, Alivelu M. Parimi, and P. Shambhu Prasad

CONTENTS 4.1 Introduction..................................................................................................... 83 4.2 MG Power System Model................................................................................ 86 4.2.1 Modeling of RTPS............................................................................... 86 4.2.2 Modeling of MGES............................................................................. 87 4.2.2.1 Modeling of WTG and PV.................................................... 87 4.2.2.2 Modeling of Diesel Engine Generator.................................. 88 4.2.2.3 Modeling of FC and BESS................................................... 88 4.2.3 Modeling of Inverter Delay, Filter Delay, and Communication Delay......................................................................... 88 4.3 MG Power System Control Architecture......................................................... 89 4.3.1 RTPS Control....................................................................................... 89 4.3.2 Distributed Primary Controller for FC and BESS...............................92 4.3.3 Distributed Primary Controller for Distributed Generation................92 4.3.4 Distributed Wind Speed-Dependent Frequency Controller for WTG...............................................................................................92 4.3.5 Hierarchical Secondary Controller and Tertiary Controller for All MGES Other Than PV.................................................................. 93 4.4 Analysis Using Simulation Studies.................................................................. 93 4.4.1 Wind Disturbance and Solar Irradiation Models................................ 93 4.4.2 Step Response under Different Parameter Disturbance Conditions....94 4.4.3 Controller Performance under Random Disturbance Parameter Variations.............................................................................................97 4.5 Conclusion....................................................................................................... 98 References.................................................................................................................99

4.1 INTRODUCTION In microgrids, power can be shared among all micro sources depending on the frequency droop characteristic that can be used for primary frequency control of the MG, where droop can vary depending on the type of micro source [1]. Secondary control, an essential part of MG frequency control, can be achieved using a load DOI: 10.1201/9781003121626-483

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frequency control (LFC) loop by designing an LFC controller for MG sources [2]. For longer time scales, tertiary frequency control can be implemented as a third control loop to achieve economic optimization of MG power dispatch [3]. Microgrids suffer from various issues like low R/X ratios, high rate of rise of frequency, large swings in frequency caused due to improper power sharing and less inertia due to high renewable energy penetration which result in frequency stability problems, even within a few seconds after a small/large disturbance [4]. In order to overcome these issues, the design of hierarchical controls that are implemented at different time frames through primary, secondary and tertiary controls (PSTC) can be identified as an essential component for successful operation of MGs [5]. Tertiary and secondary control can be centrally operated, whereas distributed control can be implemented for primary frequency control [6]. Although time frames can be differentiated for PSTC, all these controls act together to perform frequency control. Thissignifies that the design of models that can account for various control actions provided using PSTC are necessary. It can be seen from Figure 4.1 [7] that tertiary control sets the real power deviation command, ∆Pter, every 15 min to 1 hr. Secondary control adds the real power deviation command, ∆Psec, every 30 s to 15 min. Primary control adds the real power deviation command, ∆Ppri, from a few seconds to 5 min. The sum of these real power deviation commands participates in setting the controls needed to obtain the desired real power deviation for the MG. In some LFC studies, simple first-order transfer function models were considered, neglecting inverter and communication delays [8]. On the other hand, communication delay modeling is important in developing detailed models [9]. Including communication delay models in frequency control models developed for an MG results in more realistic models that can help in designing more suitable LFC controllers. Penetration of RE reduces the overall system inertia, which impacts frequency stability because of poor inertial response [10]. Virtual inertia (VI) for DFIGURE has been implemented to improve the system inertial response to obtain better frequency control [11]. However, VI for wind systems can impact system stability during the islanding condition of an MG. A recent study developed a decoupling method to suppress the negative effects of VI [12]. Although dynamic decoupling is achieved, peak deviation cannot be significantly reduced through this method.

FIGURE 4.1  Hierarchical controls—timescales.

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Moreover, it is important to consider wind speed variations and variations in solar isolation in order to address the uncertainties in power generation from RE. The MG power system control design should be insensitive to such variations. Step changes were considered to represent wind speed and solar isolation fluctuations in [8] and [13]. To mimic the real-time dynamics, more detailed models are necessary for robust frequency control design. An additional problem is restricting the MG model to a few energy sources [11–13], whereas the behavior of MG changes with the addition of another energy source. The challenges faced by MG power system frequency control can be summarized as: • Higher-order models for various energy sources considering the effect of communication delays to design robust PSTC should be given more importance. • The uncertainties in wind speed or solar irradiation are not well represented to model real-time dynamic behavior of these energy sources. • Hierarchical and distributed control architecture for MG models considering different MG sources incorporating PSTC has not been sufficiently addressed considering more detailed models. • MG frequency control during both grid-connected mode and islanded mode should be satisfactory when VI is employed for wind turbine generators. MG islanded mode of operation is widely accepted as complex in terms of frequency control because of high participation of RE with different droop characteristics. In view of the previously mentioned challenges, MG power system models and control architecture have been developed and simulated for frequency control. This chapter mainly deals with the following aspects: • Modeling the reheat thermal power system (RTPS) and MG to form an MG power system by including inverter and communication delays in order to design a robust frequency control in both operating modes. • Establishing a PSTC for frequency control by considering hierarchical tertiary and secondary controls along with distributed primary control while including the detailed modeling of MG. • Representing the RE uncertainties considering the real-time dynamic nature through developing suitable models for wind speed and solar irradiation. • Developing a wind-speed-dependent frequency controller which operates smoothly during grid-connected mode, the transition phase and islanding mode of the MG power system even under wind speed uncertainties. The remainder of this chapter is organized in the following manner. Section 4.2 deals with the detailed modeling of MG power systems, Section 4.3 describes the design and implementation of control architecture for frequency control of MG power system, Section 4.4 discusses the simulations results along with important discussion and Section 4.5 presents the conclusion.

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4.2 MG POWER SYSTEM MODEL MG power system components are depicted in Figure 4.2, where a reheat thermal power system is connected to the MG. Microgrid energy sources (MGES) are listed as follows: • • • • •

Wind turbine generator Fuel cell (FC) Battery energy storage system (BESS) PV generation Diesel generator

Modeling of RTPS and MGES is to be carried out in such a manner that the model clearly represents the changes in power deviation when subject to certain parameter variations. When the MG power system operates in grid-connected mode, RTPS participates in frequency regulation, and when the MG operates in islanded mode, the MGES alone participates in frequency regulation.

4.2.1 Modeling of RTPS RTPS, inertia and load are modeled using the following equations: ∆f =

1 ∆Pe  2HS + D

FIGURE 4.2  MG power system components.

(4.1)

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(4.2)

∆Pe = ∆Pth + ∆Pmg − ∆PL

∆Pth =

∆Pτ1 =

∆Pt =

K rTr s + 1 Tr s + 1

(4.3)

∆Pτ1

1 ∆Pτ Tτ s + 1

Kg Tg s + 1

87

(4.4)

∆Pg

(4.5)

where '∆Pe', '∆Pth ', '∆PL' and '∆Pmg' are the real power fluctuations of the MG power system, RTPS, load demand and MG. '∆Pt1', '∆Pt' and '∆Pg' are the changes in command signals in a Stage II turbine, Stage I turbine and governor, respectively. '∆f ' is the frequency fluctuation. Kr and Kg are the gain constants, and Tr, Tt and Tg are the time constants of the turbine and governor models, respectively.

4.2.2 Modeling of Mges MG power sources are modeled using the following equations: ∆Pmg = ∆Pwtg + ∆Pdg ± ∆Pbess + ∆Pfc + ∆Ppv

(4.6)

where '∆Pdg', '∆Pbess', and '∆Pfc' are the real power fluctuations in WTG, BESS and distributed generation, respectively. 4.2.2.1 Modeling of WTG and PV The wind and PV equations are specified in the following [13]. The power generated by WTG is given by

(

Pwt = 0.5 ARC pWv3

)

(4.7)

where β is air density, A R is the turbine blade swept area, Cp is the power coefficient and Wv is the variation in wind velocity. The transfer function that represents the WTG is given as Gwtg (s ) =

K wtg

(4.8)

Twtg s + 1

where Kwtg = 1 and Twtg = 1.5 are the WTG’s gain and time constants, respectively. The power generated by PV generation is given by

{

}

Ppv = ηS ϕ 1 − 0.005 (Ta + 25)

(4.9)

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where ƞ, φ, and Ta represent conversion efficiency, irradiation in kW/m2 and atmospheric temperature, respectively. The transfer function representation of PV is given by G pv (s ) =

K pv

(4.10)

Tpv s + 1

where Kpv = 1 and Tpv = 1.5 are the PV’s gain and time constants, respectively. 4.2.2.2 Modeling of Diesel Engine Generator A diesel generator can be specified by a turbine-governor model [14]. The equations that represent the turbine-governor model are given as Gdg (s ) =

(T

1

gdg

s + 1) (Ttdg s + 1)

(4.11)

where Tgdg = 0.08 and Ttdg= 0.3 are the distributed generation’s governor and turbine time constants, respectively. 4.2.2.3 Modeling of FC and BESS BESS can either charge or discharge depending on the additional or deficit power generated by RE in the MG. The transfer function model that represents the BESS is given as [13] Gbess (s ) =

Kbess Tbess s + 1

(4.12)

where Kbess= 0.03 and Tbess = 0.1 are the BESS’s gain and time constants, respectively. FCs can generate power, but some power generated by FCs is consumed by the aqua-electrolyzer (AE), and the transfer function models for FC and AE are given as G fc (s ) = Gae (s ) =

K fc Tfcs + 1 Kae Taes + 1

(4.13)

(4.14)

where Kfc= 0.01 and Tfc = 4 are the BESS’s gain and time constants, respectively, and Kae= 1 and Tae = 0.08 are the BESS’s gain and time constants, respectively.

4.2.3 Modeling of inVeRteR delay, filteR delay, and coMMunication delay The inverter, filter and communication delays can be modeled as exponential functions [2] that can be represented in Laplacian form in the MG power system model. The transfer function models are given as

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Ginv (s ) =

1 Tinv s + 1

(4.15)

G fil (s ) =

1 Tfil s + 1

(4.16)

Gd (s ) =

1 Td s + 1

(4.17)

where Tinv= 0.04, Tfil= 0.004 and Td = 0.1 are the inverter, filter and delay time constants, respectively. By considering various models, the complete block diagram representation of the MG power system model can be obtained as depicted in Figure  4.3, obtained by combining Equations (4.1)–(4.17).

4.3 MG POWER SYSTEM CONTROL ARCHITECTURE An MG power system operates through hierarchical and distributed control architecture, as depicted in Figure 4.4. The controllers employed for the MG power system control are listed in the following: • • • • • •

Primary controller for RTPS Hierarchical secondary controller for RTPS and tertiary control for RTPS Distributer primary controller for FC and BESS Distributed primary controller for distributed generation Distributed wind speed-dependent frequency controller for WTG Hierarchical secondary controller and tertiary controller for all MGES other than PV

Transport delay of the controller signal is modeled by using an equivalent transportation delay given by sTtd

Transport delay = e −

(4.18)

4.3.1 Rtps contRol Primary control is designed on the basis of droop with a regulation constant of R = 2.4Hz/MW. The equation for primary control signal is given as u1 = −

∆f R

(4.19)

where ‘∆f ’ is the change in frequency. The LFC controller is designed using a proportional-integral-derivative (PID) controller, whose equation is given as

FIGURE 4.3  Detailed model of MG power system components.

90 Microgrids

FIGURE 4.4  Hierarchical and distributed control architecture of MG power system.

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  k u2 = −∆f k p1 + i1 + kd1s    s 

(4.20)

where k p1, ki1 and k d1 are the controller constants. The tertiary control signal, α1 is derived from the tertiary frequency control loop, which is a constant feedback loop.

4.3.2 distRibuted pRiMaRy contRolleR foR fc and bess A droop cascaded PID control is designed for the FC and BESS, whose control signal can be expressed by the equation u3 =

−∆f K mg

  k + ki2 + k s   p 2 d2   s 

(4.21)

where Kmg= 0.0083 is the droop constant. kp2, ki2 and kd2 are the controller constants.

4.3.3 distRibuted pRiMaRy contRolleR foR distRibuted geneRation Based on the droop of the distributed generation governor, a droop-cascaded PID control is designed for primary control of distributed generation, and its equation is given as u4 =

−∆f Kdg

  K + ki 3 + k s   p 3 d3   s 

(4.22)

where Kdg= 0.05 is the droop constant as considered for a Type-1 plant in [15]. kp2, ki2 and kd2 are the controller constants.

4.3.4 distRibuted Wind speed-dependent fRequency contRolleR foR Wtg Wind speed is a randomly varying variable which causes large power swings in WTG generation. These large power swings result in frequency swings, which is undesirable. To control such power swings, a controller that controls the WTG generation based on the wind speed variation and frequency deviation is designed and implemented for distributed frequency control of the MG. u5 = (−∆f + ∆Wv ) (k pw + kdw s )

(4.23)

where kpw and kdw are the wind-speed controller constants. A transport delay for this controller is also introduced to account for the effect of delay of the control signal.

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4.3.5 Hierarchical Secondary Controller and Tertiary Controller for All MGES Other Than PV An LFC control loop based on PID control is incorporated for MG sources other than PV, to achieve hierarchical control of a MG whose control equation is expressed as   k u6 = −∆f k p 4 + i 4 + kd 4s  (4.24)   s

where kp4, ki4 and kd4 are the controller constants. The tertiary control signal α2 is derived from the tertiary frequency control loop, which is a constant feedback loop. Distributed control architecture helps various energy sources participate in frequency control consistent with the load disturbances or RE disturbances. Equations (4.18)–(4.24) are combined along with detailed models of MG power system components, and the control architecture is as depicted in Figures 4.3 and 4.4 and utilized for simulation studies.

4.4 ANALYSIS USING SIMULATION STUDIES The performance of various controls discussed in Section 4.3 when subject to parameter disturbance conditions while operating in different MG modes has been simulated to study the effectiveness of the proposed control architecture by including the detailed models discussed in Section  4.2. The response of the MG power system under different conditions can be analyzed through various simulations under different disturbance parameter conditions. The models discussed in Figures 4.3 and 4.4 are used for analysis in MATLAB/Simulink for dynamic time domain analysis. The disturbance parameter conditions for time domain analysis while performing simulation studies are listed in the following: • • • • •

A step load disturbance of 10% Wind speed disturbance of (8–11) m/s Solar irradiation levels increasing from 800 to 1000 w/m2 Solar irradiation levels decreasing from 800 to 400 w/m2 The disturbance parameter conditions are verified during two modes of MG operation: • MG power system in grid connected mode • MG power system in islanded mode

4.4.1 Wind Disturbance and Solar Irradiation Models The disturbance model is shown in Figure 4.5. An interval and pulse generator block is used to simulate the time and amplitude of the wind speed or solar irradiation disturbance. A rate limiter is used to define the rate of rise and fall of the disturbance pulse to mimic the real-time dynamic changes of wind speed and solar irradiation.

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FIGURE 4.5  Wind disturbance model.

FIGURE 4.6  Deviations in (a) wind speed (b) solar irradiation.

The simulated changes in wind speed and solar irradiation are depicted in Figure 4.6. These models can represent the changes in a more appropriate way than considering a step disturbance.

4.4.2 Step Response under Different Parameter Disturbance Conditions The performance of controllers should be such that they can respond to sudden and large step load changes so as to maintain frequency of the system during either gridconnected or islanded mode of operation. The response of controllers under various wind speed and solar irradiation changes should be suitable to maintain the frequency at nominal values. To study the performance of controllers, various dynamic time-domain simulations are necessary to analyze the frequency deviation under different conditions. Simulation studies are presented by considering a wind speed, solar irradiation and step load disturbances and also by including a transport delay in the controller signal. Initially, performance of distributed primary controllers is analyzed. For a 10% step load change at t = 5s and wind-speed variation from 8 to 11 m/s at t = 0 s, with primary control acting alone on the MG power system, peak deviation is reduced by 25% when the solar irradiation is reduced from 0.8 to 0.4 P. U as depicted in Figure  4.7. It is evident that the amount of solar irradiation change can affect the transient frequency deviation of the MG power system.

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FIGURE 4.7  Step response of MG power system for (a) wind speed deviation from (8–11) m/s and solar irradiation deviation from 800 to 1000 w/m2 (b) wind speed deviation from (8–11) m/s and solar irradiation deviation from 800 to 400 w/m2.

As a next step, the performance of secondary and tertiary control during islanded mode is considered for analysis. It can be seen from the step response depicted in Figure 4.8 that when hierarchical control is implemented in the MG power system, along with distributed primary control, frequency oscillations are damped during islanded mode of operation considered at t=50s. Peak overshoot in frequency deviation of the MG power system is mainly because of the changes in wind speed. To address this issue, a wind speed-dependent controller is employed, whose step response is depicted in Figure 4.8, which reduces the peak deviation by close to 90%, which proves the effectiveness of the designed wind speed-dependent controller. The transition to islanded mode is very smooth, with reduced peak overshoot during islanded operation when compared to distributed primary and hierarchical controls acting for frequency control. The effect of transport delay on the wind-speed-dependent controller is a significant component to be studied. In Figure 4.9, a transport delay of 0.1s is considered for the controller to obtain the step response. When the transport delay in wind-speed-dependent controller signal transmission is increased from 0.1 to 0.3s, the peak deviation in frequency is increased by 73%, as shown in Figure 4.10. It can be understood from this example that a transport delay in the control signal can significantly impact the controller performance

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FIGURE  4.8  Step response considering secondary and tertiary control during islanded mode.

FIGURE 4.9  Step response of MG power system while implementing wind speed-dependent frequency controller.

FIGURE 4.10  Effect of transport delay in wind-speed controller signal on step response of MG power system.

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4.4.3 Controller Performance under Random Disturbance Parameter Variations A wind profile varying between 7 and 11 m/s in a random manner is considered to analyze the controller performance. Random variations in wind speed can closely replicate the real-time wind patterns for which the effectiveness of the controller can be studied. The random variations of a highly varying wind profile have been considered in the wind-speed model and wind-speed pattern, as shown in Figure 4.11(a). For the wind profile shown in Figure 4.11(a), the amount of wind power variation from WTG is in the range of ±0.2 P.U, as depicted in Figure 4.11(b); the frequency deviation is very minimal; and the frequency is close to nominal frequency, which clearly indicates the effectiveness of the wind-speed-dependent controller in achieving robust frequency control, which is shown in Figure 4.11(c). The load variation is also a random phenomenon, and the design of control architecture should compensate for such load changes in order to maintain real power balance.

FIGURE 4.11  Deviation in (a) wind-speed (b) real power of WTG (c) frequency of MG power system.

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FIGURE 4.12  Deviation in (a) frequency (b) real power of MG power system.

Along with wind profile, a random load profile is also considered while islanding the MG power system at t = 0s, as depicted in Figure 4.12(a) and (b). The frequency deviation is minimal and the real power deviation of MG within the range of ±0.025 P.U. The load variation considered dynamically varies with respect to time within a range of ±0.015 P.U. The response of controllers is quick and robust when timevarying load disturbance is considered.

4.5 CONCLUSION Modeling of MG power systems considering inverter, communication and transport delays is essential for establishing more detailed models which can be utilized for designing a suitable control architecture. Depending on the droop of the MG energy source, distributed primary controls are designed, which are coordinated by hierarchically controlled secondary and tertiary controls. Wind speed uncertainties, which are the primary cause of large frequency swings, can be controlled by implementing a distributed wind speed-dependent controller. The designed control architecture is analyzed using various simulation studies. A 90% reduction in the peak frequency swing has been observed at a transport delay of 0.1 s. Random changes in load or wind speed in the case of islanded mode were efficiently handled by the MG power system control through limiting the power swings in the microgrid to a range of ±0.025 P.U.

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REFERENCES 1. 2. 3.

4. 5. 6. 7. 8.

9. 10. 11.

12. 13. 14.

15.

R. H. Lasseter, “MicroGrids,” 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309), vol. 1 (New York, 2002): 305–308. N. Vafamand, M. H. Khooban, T. Dragičević, et  al., “Time-Delayed Stabilizing Secondary Load Frequency Control of Shipboard Microgrids,” IEEE Systems Journal, vol. 13, no. 3 (Sept. 2019): 3233–3241, doi: 10.1109/JSYST.2019.2892528. Z. Li, Z. Cheng, J. J. Liang, J. Si, L. Dong, et  al., “Distributed Event-Triggered Secondary Control for Economic Dispatch and Frequency Restoration Control of Droop-Controlled AC Microgrids,” IEEE Transactions on Sustainable Energy (2019), doi: 10.1109/TSTE.2019.2946740. M. Farrokhabadi, et  al., “Microgrid Stability Definitions, Analysis, and Examples,” IEEE Transactions on Power Systems, vol. 35, no. 1 (Jan. 2020): 13–29. B. Kirby, E. Ela, and M. Milligan, “Analyzing the Impact of Variable Energy Resources on Power System Reserves,” Renewable Energy Integration, vol. 13, no. 6 (Elsevier, 2017): 85–101. S. T. Cady, A. D. Domínguez-García, and C. N. Hadjicostis, “A Distributed Generation Control Architecture for Islanded AC Microgrids,” IEEE Trans. on Control Systems Technology, vol. 23, no. 5 (Sept. 2015): 1717–1735, doi: 10.1109/TCST.2014.2381601. P. Chilukuri, et  al., “Introduction of Secondary Frequency Control in Indian Power System,” 2018 20th National Power Systems Conference (NPSC) (Tiruchirappalli, India, 2018): 1–6, doi: 10.1109/NPSC.2018.8771767. T. Mahto, H. Malik, and M. Saad Bin Arif, “Load Frequency Control of a SolarDiesel Based Isolated Hybrid Power System by Fractional Order Control Using Partial Swarm Optimization,” Journal of Intelligent & Fuzzy Systems (JIFS), vol. 35 (2018): 5055–5061. S. Liu, X. Wang, and P. X. Liu, “Impact of Communication Delays on Secondary Frequency Control in an Islanded Microgrid,” IEEE Trans. on Industrial Electronics, vol. 62, no. 4 (Apr. 2015): 2021–2031, doi: 10.1109/TIE.2014.2367456. H. Bevrani, B. Francois, and T. Ise, Microgrid Dynamics and Control (New York: Wiley, 2017). X. Tian, W. Wang, Y. Chi, Y. Li, and C. Liu, “Virtual Inertia Optimisation Control of DFIGURE and Assessment of Equivalent Inertia Time Constant of Power Grid,” IET Renewable Power Generation, vol. 12, no. 15 (19 Nov. 2018): 1733–1740, doi: 10.1049/ iet-rpg.2018.5063. J. Xi, H. Geng, and X. Zou, “Decoupling Scheme for Virtual Synchronous Generator Controlled Wind Farms Participating in Inertial Response,” Journal of Modern Power Systems and Clean Energy (2019) doi: 10.35833/MPCE.2019.000341. K. Singh, M. Amir, F. Ahmad and M. A. Khan, “An Integral Tilt Derivative Control Strategy for Frequency Control in Multimicrogrid System,” in IEEE Systems Journal, (2020), doi: 10.1109/JSYST.2020.2991634. Y. Mi, Y. Song, Y. Fu, X. Su, C. Wang, and J. Wang, “Frequency and Voltage Coordinated Control for Isolated Wind–Diesel Power System Based on Adaptive Sliding Mode and Disturbance Observer,” IEEE Trans. on Sustainable Energy, vol. 10, no. 4 (Oct. 2019): 2075–2083, doi: 10.1109/TSTE.2018.2878470. C. Wu, et  al., “Studies on Operation Modes of Diesel Generators in a Standalone Power System,” 2018 4th International Conference on Green Technology and Sustainable Development (GTSD) (Ho Chi Minh City, 2018): 72–76, doi: 10.1109/ GTSD.2018.8595556.

5

Hybrid Microgrid Design Based on Environment, Reliability, and Economic Aspects Sriparna Roy Ghatak, Aashish Kumar Bohre, and Parimal Acharjee

CONTENTS 5.1 Introduction................................................................................................... 102 5.2 Uncertainty Modeling.................................................................................... 103 5.2.1 Probabilistic Model of Solar Irradiance............................................ 103 5.2.2 Probabilistic Modeling of Wind Speed............................................. 104 5.2.3 Load Forecasting............................................................................... 105 5.3 Battery Storage Model................................................................................... 105 5.4 Scenario Modeling Using Data Clustering.................................................... 106 5.5 Modeling of Fitness Function........................................................................ 106 5.5.1 Reliability Assessment of Microgrids............................................... 106 5.5.2 Economic Aspects............................................................................. 106 5.5.3 Environmental Aspects...................................................................... 107 5.6 Security Constraints...................................................................................... 108 5.6.1 Voltage Constraints............................................................................ 108 5.6.2 Power Flow Constraints..................................................................... 108 5.6.3 Power Equality Constraints............................................................... 108 5.7 Design Problem Formulation......................................................................... 108 5.8 Multi-Objective Algorithm............................................................................ 109 5.8.1 Theory of Parental Inheritance.......................................................... 109 5.8.2 Non-Dominant Sorting...................................................................... 109 5.8.3 Crowding Distance............................................................................ 109 5.8.4 Dynamic Crowding Distance............................................................ 109 5.8.5 Compromise Solution........................................................................ 110 5.9 Case Study..................................................................................................... 110 5.10 Conclusion..................................................................................................... 114 References............................................................................................................... 117

DOI: 10.1201/9781003121626-5101

102

Microgrids

5.1 INTRODUCTION In recent years, due to high climatic and environmental concerns, countries across the globe are vastly shifting towards renewable and sustainable sources to meet their increasing energy demands. In this context, the concept of the microgrid, which consists of distributed energy sources, storage devices and controllable loads [1], is gaining huge importance worldwide. Microgrids are regarded as a critical technology which can enhance the resilience of power systems. They can operate either in grid-connected mode or independent or islanded mode [2]. A microgrid consists of the integration of various types of distributed generation units, such as a solar energy source, wind, fuel cells and so on, and interconnected loads and acts as single controllable entity [3]. Renewable energy sources such as solar and wind are uncertain or intermittent and mostly depend on additional aspects such as climatic and meteorological conditions and time of day [4]. In these circumstances, a hybrid system consisting of a combination of renewable sources such as solar and wind, along with storage in a microgrid environment, is an effective means to reduce environment pollution and meet energy demands. System uncertainty mainly arises due to intermittent renewable energy resources such as solar and wind. Solar and wind power is random in nature since its power output depends on atmospheric conditions such as solar irradiance, temperature, wind speed and so on [5–6]. A hybrid system consisting of two or more distributed energy sources can make the microgrid resilient to weather changes or fuel supply chain disruptions [7–8]. Energy storage devices are an important component of microgrids. They store the excess energy renewable energy when there is a surplus and dispatch it appropriately when needed [9–10]. For successful implementation and gradual commercialization of renewable-based microgrid systems, it is essential to develop a robust design framework considering a high level of system uncertainty and reliability. The optimal design of the system includes identifying the most suitable configuration and selecting the optimal capacities of the storage devices and energy sources [11]. Designing a microgrid is a complicated task, as it needs development of accurate mathematical models intended for each system component [12]. Capacity optimization of renewable sources is an important aspect of robust system design, which refers to determination of the optimal number of components such that system reliability is maximized and total cost of the system is minimized [13]. Oversizing a component may ensure high system reliability, but the economic benefit will be reduced. The unit sizing of generating sources has a huge impact on system efficiency [13]. Effective energy management and control is critical for proper microgrid operation. The primary aim of any efficient energy management strategy is to provide a stable supply of energy to consumers. A smart energy management system receives realtime data from the system, processes it and makes the appropriate control decision quickly [14]. The optimal design problem of microgrids is highly complex, nonlinear and nonconvex in nature and requires an efficient and robust optimization algorithm [13]. As multiple objectives are considered for the optimal design problem of microgrids, instead of conventional heuristic process in which various objectives are converted to a single objective problem, an effective multi-objective algorithm should be used [15–16].

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In this work, an uncertainty modeling framework of renewable sources such as solar and wind is interfaced with a multi-objective optimization scheme for the development of a viable design tool for a microgrid system. A battery system is considered as the storage system available in the proposed design model. Effective energy management strategies are proposed to make a secure and reliable supply of energy to load. It would be computationally expensive to consider all the uncertain scenarios of renewable energy and load while designing the microgrid. On the other hand, if all these scenarios are not considered, then the design model of the microgrid will not represent its true stochastic characteristic. Therefore, clustering techniques such as k-means clustering [16] are used in this work to lower the number of scenarios to a representative number that reflect the true stochastic nature of the microgrid. The design problem is resolved considering maximization of economic profit, reliability and environmental aspects. Security constraints of voltage, power flow and power equality are also considered in the proposed design strategy. A multi-objective algorithm, the extended non-dominant sorted genetic algorithm (E_NSGAII) [16], is used in this work to find the optimal capacity and location of solar, wind and battery storage systems considering the maximization of economic benefits, environment, and reliability. The proposed design model is tested in an IEEE 13 bus system [17] which is considered a microgrid for validation of the proposed design framework.

5.2 UNCERTAINTY MODELING Modeling of intermittent solar energy sources and wind power is an important aspect of microgrid design [5, 16]. The modeling of hybrid energy sources consisting of solar, wind and a storage system is given as follows.

5.2.1 pRobabilistic Model of solaR iRRadiance Currently, solar energy source has emerged as an appropriate choice for a sustainable energy supply in microgrids. The output of solar energy sources is highly uncertain in nature and varies with time, weather conditions, solar irradiance and temperature. This uncertainty or intermittent characteristics of solar energy sources creates challenges for system operators in balancing generation with load demand and maintaining system constraints. Therefore, in order to design a safe and reliable microgrid, it is essential to model the solar energy source considering its randomness. To express the random solar output, a beta probability density function f (s t ) [16] is used.  Γ (a + b )  t a t b t (s ) −1 (1 − s ) −1 if 0 ≤ s ≤ 1 a,b > 0 f (s t ) =   Γ (a ) Γ (b )   0 otherwisee 

(5.1)

The shape parameters are represented as a and b and can be expressed as follows a=

µ ×b 1− µ

b = (1 − µ) × ((µ(1 + µ)) / σ 2 − 1)

(5. 2)

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In Equation (5.2), σ and µ represent the standard deviation and mean, which are evaluated from historical data. The maximum power P (s ) of a PV system is formulated as follows. (5.3)

P (s ) = N × FF ×V ×1

N in Equation (5.3) represents the module number of PV panels. FF is the fill factor, and V and I are the voltage and current of the solar array as given in Equations (5.4) and (5.5). V = VOc − KVoltage ×TCell

(5.4)

I = s t [I S + K I × (TCell − 25) ]

(5.5)

VOc and I S in Equations (5.4) and (5.5) are the voltage on an open circuit and current on a short circuit, respectively. TCell , K I and KVoltage are the cell temperature of the solar panels and coefficient of temperature of current and voltage, respectively. To enhance the accuracy of the outcome, every hour is segregated into a number of states (NS). The probability of all the states is evaluated as given in Equation (5.1). The expected hourly output of solar power is calculated as follows: P (s )hour =

∫

NS

i=1

( )

( )

f s t ×Po s t

(5.6)

5.2.2 pRobabilistic Modeling of Wind speed Wind energy systems are another promising renewable energy source used in microgrids. The output of wind energy is variable or uncertain, as it is dependent on wind speed, which in turn depends on weather conditions, geographical location and so on. Therefore, it is essential to model the wind energy resource considering the system uncertainty. To express the randomness in wind speed, a Weibull probability distribution function [18–19] can be used as expressed in the following. K −1

K  w    C C 

fs (w ) =

  K   w exp −    C  

−1.086

σ  K =    µ 

;C =

(

µ

Γ 1+ 1

k

)

(5.7)

(5.8)

w, K and C in equation (5.7) represent wind speed and parameters representing shape and scale, respectively. σ and µ represent the standard deviation and mean of wind speed, which can be collected from historic data. Ns

Pwtt = ∑Pwts × pw (s ) s=1

(5.9)

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Hybrid Microgrid Design

pw (s ) =

∫

wsu

f (w )dw

(5.10)

wsl s

Pwts and pw (s ) are the wind power output and the Weibull probability of wind speed, respectively, in the states. wsu and wsl are the upper and lower limits of wind speed.

5.2.3 load foRecasting Power demand in microgrids exhibits distinctive uncertainty, as it varies with time, season and so on. To model the random nature of real and reactive loads, a probabilistic method of the Gaussian normal distribution ( f l ) [16] can be used, which is t given as follows.

()

2

f (lt ) =

1 σ 2π

−(lt −µ)

e

(5.11)

2σ 2

σ and µ in Equation (5.9) represent the standard deviation and mean of historic load data. P (t ) = Po(t ) (1 + growth )k + f (lt )σ

(5.12)

Q(t ) = Qo(t ) (1 + growth )k + f (lt )σ

(5.13)

P(t) and Q(t) are the expected real and reactive power at time t. Po(t) and Qo(t) are the real and reactive power measured with a smart meter at time t. In Equations (5.10) and (5.11), growth represents load growth in the future and is considered in the process of designing the microgrid.

5.3 BATTERY STORAGE MODEL Storage devices such as batteries are an extremely important aspect of microgrids. Battery storage devices are used to store the excess power of renewables and can be suitably used afterwards when needed in the system. In recent years lithium-ion batteries [9] have become a widely accepted storage solution for microgrids. The mathematical equation of energy storage during charging and discharging of battery storage W (t )B at a given time t is given as follows. W (t ) = W (t − 1) + ηcharging × Pch ∆t B

B

W (t ) = W (t − 1) − B

B

Pdis ∆t ηdisch arging

(5.14)

(5.15)

Pch and Pdis are charging and discharging power, respectively. η and ηcharging disch arging are the discharging and charging efficiency of the battery storage system, respectively.

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Microgrids

The battery state of charge (SOC) is an important parameter for energy management of microgrids consisting of hybrid energy sources. For safe and reliable operation of the microgrid battery, the SOC should be within permissible limits.

5.4 SCENARIO MODELING USING DATA CLUSTERING System uncertainty is an integral part of microgrids because of the high penetration of intermittent renewable energy sources in it. Additionally, system load is also uncertain and varies with time, consumer activities and so on. A group of clusters is made in this method in such a manner that the mean square distance from every data point of the original set and the representative data point of the created cluster known as the centroid is the minimum, as given in Equation (5.16). k

J =

n

∑∑ (x

− ck )

2

i

(5.16)

i=1 x i

where c represents the centroid of the cluster. k

5.5 MODELING OF FITNESS FUNCTION The detailed modeling of the fitness function is given as follows.

5.5.1 Reliability assessMent of MicRogRids The reliability of microgrids is an important aspect which should be considered for system design. A reliable hybrid microgrid system should have the capability to supply consistent power to the load for a given period. Expected energy not served (EENS) can be used as the reliability index [16] in the microgrid and is precisely expressed as follows m

EENS = ∑ i=1U i × Pi

(5.17)

where U represents unavailability and P represents real power load. i i

5.5.2 econoMic aspects The unit cost of component i( C i ) includes installation cost, replacement cost, salvage cost, operation cost and maintenance cost. In a microgrid, the components that are used are PV panels, wind system, battery storage converters and inverters.

{

C i = Initialcost i * P + Ri * CCP * P + O & MC * CCP * YRspan * P − S

}

(5.18)

where Initialcost i , O & MC R , S represents preliminary installation cost/KW, operai tion and maintenance cost, replacement cost and cost related to salvage, respectively. Cumulative current price (CCP) is allied with the variable cost of such operation and

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Hybrid Microgrid Design

maintenance. CCP as termed in [16] is used to represent the complete costs during the given span of time. CCP is featured for expected future cash flows considering the rate of inflation and interest. Considering the inflation rate (IFL) and interest rate (INR), the current price (CP) is estimated as follows: CP=

1 + IFL

100 INR 1+ 100

(5.19)

From CP, CCP is estimated for microgrid design as follows.

CCP = 

Si = Ri

1 − CP

YRspan

100

(5.20)

1 −CP RL

(5.21)

YRspan

where Si is the salvage cost of the component i and RL is the remaining life. The total cost Totalcost incurred is mathematically expressed as follows. Totalcost = N PVC pv + N windC wind + N batteryC battery + N inverterC inveerter + CEENS

(5.22)

where N PV , N wind , N battery and N inverter refer to number of PV panels, wind turbine, battery and inverter. CEENS is the cost of energy not served. The revenue earned is from selling electric energy to consumers. N

Totalbenefit = ∑ PscC tariff ×CCP

(5.23)

sc=1

The benefit cost ratio (BCR) of microgrid design can be calculated as BCR = Benefit

(5.24)

Cost

From Equation (5.24), it is obvious that a high BCR implies high economic benefits.

5.5.3 enViRonMental aspects To reduce pollution and encourage the usage of renewables, countries across the world are imposing carbon discharge rates on consumers and producers. For microgrids, to judge the environmental benefits due to integration of renewables, the environment benefit index (EBI) is used. The mathematical expression of EBI is given as follows. EBI =

(CE )

without _ renewable

(CE )

− (CE )

with _ renewable

without _ rene n wable

(5.25)

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Microgrids

where (CE ) and (CE ) are the cost of environmental emissions without _ renewable with _ renewable without and with the use of renewables in the microgrid, respectively. The higher the EBI ratio, higher the environmental benefits due to incorporation of renewables in the microgrid.

5.6 SECURITY CONSTRAINTS Abiding by security constraints is the most important aspect for safe, secure operation of microgrids. Therefore, security constraints must be considered while designing the microgrid. Some of the security constraints that should be considered are as follows.

5.6.1 Voltage constRaints In microgrids, the voltage profile of the system should be within the upper and lower limits as given in Equation (5.26). Vi

min

≤ Vi ≤ Vi

max

(5.26)

5.6.2 poWeR floW constRaints Power flow should also be within limits, as given in equation (5.27). Si ≤ Simax

(5.27)

5.6.3 poWeR equality constRaints At every time instant t, the total power generation should be equal to total load, as given in equation (5.28). Pgrid,t + Prenewable,t − Pload ,t = 0

(5.28)

5.7 DESIGN PROBLEM FORMULATION In this chapter, a design problem of microgrids is resolved considering the maximization of economy, ecological benefits and reliability while considering security constraints as given in Section 5.3. Financial and ecological parameters such as cost of renewables, power tariff, grid power cost, inflation rate and interest rate are considered as the input to the problem. The reliability index, EENS, is already integrated in BCR and therefore not distinctly taken as a separate objective function. The design strategy consists of decisions concerning the number of devices to be used to maximize the objectives. To enhance the accuracy of the obtained results, the uncertainty of renewables and load is considered in the proposed problem.

Hybrid Microgrid Design

109

5.8 MULTI-OBJECTIVE ALGORITHM In microgrid design, the objectives to be fulfilled may be contradictory in nature. Therefore, it is essential to utilize a robust multi-objective algorithm to solve it. The extended non-dominant sorted genetic algorithm is utilized in the current process to solve the proposed design problem. Various operators of E_NSGAII are as follows.

5.8.1 Theory of Parental Inheritance An improved pool of parents improves the convergence speed to the global Pareto front. Therefore, in E_NSGAII, chromosomes with the best results in terms of objective functions are selected and copied to the parent population. The process of creating the improved pool continues until the chosen size of the population is reached. If the problem is a single objective of population size N, it should be generated N times to create the pool. Along similar lines, if it is a multi-objective problem of P objectives, then number of generations will be N/P. Therefore, it is evident that the time for computation reduces with an increase in the number of fitness functions.

5.8.2 Non-Dominant Sorting It is a process to rank solutions. The non-dominant solution in a pool is the solution that is not dominated by any other solution in terms of the fitness function. Every solution of the first front is given a rank of 1. These first front solutions are momentarily ignored and the next set of non-dominant solutions is found in the population. They are assigned a rank of 2 and are members of the second front. This process continues and stops when every member of the population pool is allocated a rank.

5.8.3 Crowding Distance Crowding distance (di) is required to provide an estimate of the solution density adjacent to a particular solution i in the pool of the population. Solutions with high di are preferred, as they are placed in a low crowding zone. Thus, solutions with a large crowding distance are preferred to maintain diversity.

5.8.4 Dynamic Crowding Distance To maintain diversity, the concept of dynamic crowding distance (Di), as proposed in [6], is used in which the calculations for individuals are done adaptively. Di is calculated in the non-dominant set for every solution, and among them, the solutions having the lowest Di are discarded. Subsequently Di is recalculated for the rest of the solutions, and the process continues until the requisite number of solutions is selected from the set of non-dominant solutions. The mathematical expression of Di is as follows. Di =

di ln

1 Vi

(5.29)

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Microgrids

5.8.5 coMpRoMise solution To select a compromise solution out of all the acquired Pareto solution set, fuzzy logic [20] can be used. The compromise solution should be at a high distance from the worst solutions and a small distance from the ideal solution. The mathematically fuzzy membership function of the jth solution is expressed as follows. µij =

OFi max −OFi OFi max −OFi min

(5.30)

In equation (30), OF represents the objective functions. As the proposed design problem is a maximization problem, the maximum value of the objective function (OFi max ) represents the best solution, and the minimum value of the objective function OFi min represents the worse solution.

5.9 CASE STUDY In the present design problem, an IEEE 13 bus test system is considered as a microgrid. This system consists of two capacitors; one is connected at bus number 675 and another at bus 611. The microgrid system also consists of a voltage regulator placed between 650 and 632. A diagram of the test microgrid is shown in Figure 5.1. The E_NSGAII algorithm, as in [6], is used to resolve the problem in this work. Parent inheritance, non-dominant sort and dynamic crowding distance procedures are used in the multi-objective optimization. For the process of selection, the tournament

FIGURE 5.1

Single line diagram of IEEE 13 bus test system.

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Hybrid Microgrid Design

selection method is used. The simulated binary crossover and polynomial mutation process are used for the crossover and mutation process in the E_NSGAII algorithm, respectively. Fuzzy logic is used to obtain the single compromise solution among the obtained Pareto solutions. Table 5.1 shows the parameters utilized in the multi-objective E_NSGAII process. The present design problem is framed considering a time span of 15 years. Table 5.2 shows the environment and economic data used in this work. Figure 5.2 shows the representative scenarios considered in a year using k-means clustering. The historic data of solar, wind and load of four seasons (summer, winter, spring and autumn) are collected. The collected data is then divided into night and TABLE 5.1 Parameters Used in E_NSGAII Parameters

Values

Iteration number

450

Population size

100

Crossover Probability

0.9

Mutation Probability

0.5

Crossover Index

3

Mutation Index

18

TABLE 5.2 Economic and Environment Data Parameters

Values

CECO2

10 $/ton CO2

Inflation rate

10%

Interest rate

10%

PV Capital cost and replacement cost

1084$/KW

Operation and maintenance (O&M) cost

5$/year

Lifetime

20 years

Wind Capital cost and replacement cost

600$/KW

O&M cost

0.02$/KWh

Lifetime

20 years

Inverter Capital cost and replacement cost

127$/KW

Battery Capital and replacement cost

600$/KWh

O&M cost

1$/year

Lifetime

8years

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Microgrids

FIGURE 5.2  Representative scenarios.

day blocks. The value of K is chosen as 12, data clustering is used in every block and 96 representative scenarios are obtained in a year. In Table 5.3, an energy management strategy of a hybrid energy source with an energy storage device is shown. The 24  hours in a day are divided into two time spans, 6 a.m. to 6p.m. and then 6 p.m. to 6 a.m. Each time period is divided into two power conditions. Case I: When total generation is more than demand. Case II: When total demand is more than generation. Battery SOC is the most important control parameter, and accordingly switching decisions are made. For each case, five conditions of battery SOC are considered. The symbol     indicates that the device is on, and the symbol     indicates that the device is off in Table 5.3. Similarly, the symbol  represents the charging status and discharging of the battery. Battery SOC is maintained between 40 and 90, and whenever the battery SOC reaches less than 40, the battery is charged. In this work grid, the connection is mostly switched off to ensure autonomous operation of the system. As shown in Table 5.3, the grid is switched on only during the night when both power from the battery and wind is insufficient to supply the load. At every point in time, safe and reliable power supply to the load will be ensured. Figure 5.3 shows the flowchart for the implementation of the E_NSGAII algorithm. Figures 5.4 and Figure 5.5 show the expected hourly variation of solar output and wind output obtained using the beta probability density function and Weibull function as given in Equations (5.1) and (5.7), respectively. As is observed in Figure 5.4, the solar output is maximum at 1p.m. and reduced to 0 from 7p.m. onwards. In a

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Hybrid Microgrid Design

TABLE 5.3 Energy Management Strategy of the Hybrid Energy Source System Time

Power Condition

PV Status

Battery SOC

Wind Status

Battery Status

Grid

6 a.m.–6p.m.

Pgen≤ Pload



90–80









80–60









60–80









40–60









Less than 40









90–80









80–60









60–80









40–60









Less than 40









90–80







80–60









60–80









40–60







Pload≤ P gen

6 p.m.–6a.m.

Pgen≤ Pload

Pload≤ P gen



Less than 40









90–80









80–60









60–80









40–60









Less than 40







similar way, the wind power output varies throughout the day and totally depends on wind speed. Table 5.4 refers to the optimal location and size allocated for hybrid energy sources and battery energy storage. The energy sources and storage devices are allocated at the same location, as observed from Table 5.4. In Table 5.5, the objective function values, BCR and EBI, of the five best solutions are shown. As given in Section 5.8, the Pareto solutions with the highest value of dynamic crowding distance are the best solutions. However, solution 3 is the best one with respect to EBI and solution 4 for BCR. Therefore, it becomes essential to find one compromise solution among the best acquired solutions. The compromise solution is selected by using fuzzy logic, as in Section 5.5. Solution 2 is selected in this work as the compromise one and is marked bold in Table 5.5. It is clearly observed from the objective function values obtained in Table 5.5 that the optimum allocation of hybrid renewable sources and storage device provides economic and environment benefits. Figure  5.6 shows the

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Microgrids

FIGURE 5.3  Flowchart.

optimal Pareto curve obtained through applying the multi-objective optimization procedure E_NSGAII. The figure shows all the obtained non-dominant curves using the multi-objective E_NSGAII procedure.

5.10 CONCLUSION A robust design framework of a microgrid consisting of hybrid energy sources and battery storage is proposed in this chapter. The present design model aims to maximize financial, reliability and environmental benefits while considering the security constraints of the microgrid. Practical features such as renewables and load uncertainty are considered in this design structure. To reduce the complexity arising due

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FIGURE 5.4  Hourly variation of solar power.

FIGURE 5.5  Hourly variation of wind power output.

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TABLE 5.4 Optimal Location and Configuration of PV, Wind and Battery Storage Location

Number of PV

Number of Wind

Number of Battery

671

20

10

5

675

25

15

10

652

15

10

5

TABLE 5.5 Objective Function Value of Best Pareto Solutions Solutions

EBI

BCR

1

0.8112

4.824

2

0.8134

4.162

3

0.8312

5.271

4

0.7840

5.822

5

0.7520

2.811

FIGURE 5.6  Pareto optimal curve.

to a large number of operating scenarios, k-means data clustering is utilized in this chapter. The fuzzy logic-based multi-objective E_NSGAII is successfully applied to find the optimal location and configuration of the renewable energy source and storage system. An IEEE 13 bus system is used as the test case for implementation of the design model. The results clearly indicate that the implementation of the present design methodology enhances the financial, reliability and ecological benefits of the system.

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REFERENCES 1. P. Yang, and A. Nehorai. “Joint optimization of hybrid energy storage and generation capacity with renewable energy.”  IEEE Transactions on Smart Grid  5 no. 4 (2014): 1566–1574. 2. J. Nelson, N. G. Johnson, K. Fahy, and T. A. Hansen. “Statistical development of microgrid resilience during islanding operations.” Applied Energy 279(2020): 115724. 3. S. Newman, K. Shiozawa, J. Barrett, E. Douville, T. Hardy, and A. Solana. “A comparison of PV resource modeling for sizing microgrid components.” Renewable Energy 162 (2020):831–843. 4. P. Gangwar, S.N. Singh, and S. Chakrabarti. “Multi-objective planning model for multiphase distribution system under uncertainty considering reconfiguration.” IET Renewable Power Generation 13, no. 12 (2019): 2070–2083. 5. M. Fan, V. Vittal, G. T. Heydt, and R. Ayyanar. “Probabilistic power flow studies for transmission systems with photovoltaic generation using cumulants.” IEEE Transactions on Power Systems 27 (2012): 2251–2261. 6. F. J. Ruiz-Rodriguez, J. C. Hernandez, and F. Jurado. “Probabilistic load flow for radial distribution networks with photovoltaic generators.”  IET Renewable Power Generation (2012): 110–121. 7. S. Sannigrahi, S. R. Ghatak, and P. Acharjee. “Multi-scenario based bi-level coordinated planning of active distribution system under uncertain environment.” IEEE Transactions on Industry Applications 56, no. 1 (2020): 850–863. 8. P. Kayal, and C. K. Chanda. “Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network.” Renewable Energy 75 (2015): 173–186. 9. D. Q. Hung, N. Mithulananthan, and R. C. Bansal. “Integration of PV and BES units in commercial distribution systems considering energy loss and voltage stability.” Applied Energy 113 (2014): 1162–1170. 10. Y. Zheng, D. J. Hill, and Z. Y. Dong. “Multi-agent optimal allocation of energy storage systems in distribution systems.” IEEE Transactions on Sustainable Energy 8 no. 4 (2017): 1715–1725. 11. M. M. Samy, S. Barakat, and H. S. Ramadan. “A flower pollination optimization algorithm for an off-grid PV-Fuel cell hybrid renewable system.” International Journal of Hydrogen Energy 44 (2019): 2141–2152. 12. S. Singh, P. Chauhan, M. A. Aftab, I. Aftab, S. M. Hussain, and T. S. Ustun. “Cost optimization of a stand-alone hybrid energy system with fuel cell and PV.” Energies 13 (2020): 1295. 13. S. Singh, P. Chauhan, and N. Singh. “Capacity optimization of grid connected solar/ fuel cell energy system using hybrid ABC-PSO algorithm.” International Journal of Hydrogen Energy45(February 23, 2020): 10070–10088. 14. J. Pascual, J. Barricarte, P. Sanchis, and L. Marroyo. “Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting.” Applied Energy 158 (2015): 12–25. 15. S. Jeyadevi, S. Baskar, C. K. Babulal, and M. W. Iruthayarajan. “Solving multiobjective optimal reactive power dispatch using modified NSGA-II.” International Journal of Electrical Power & Energy Systems 33, no. 2 (2011): 219–228. 16. S. R. Ghatak, S. Sannigrahi, and P. Acharjee. “Multi-objective framework for optimal integration of solar energy source in three-phase unbalanced distribution network.” IEEE Transactions on Industry Applications 56, no. 4 (2020): 3068–3078. 17. Andres Julian Aristizabal, Henry Giovanni Pinilla, and Carlos Andrés Forero. “Modeling of distributed power systems in 13 nodes IEEE electric grids.” Periodicals of Engineering and Natural Sciences 4, no. 2 (2016).

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18. M. Aien, M. G. Khajeh, M. Rashidinejad, and M. Fotuhi-Firuzabad. “Probabilistic power flow of correlated hybrid wind-photovoltaic power systems.” IET Renewable Power Generation 8, no.6 (2014): 649–658. 19. S. Sannigrahi, S. Roy Ghatak, and P. Acharjee. “Strategically incorporation of RES and DSTATCOM for techno-economic-environmental benefits using search space reduction-based ICSA.” IET Generation, Transmission & Distribution 13 no. 8 (2019): 1369–1381. 20. H. B. Tolabi, M. H. Ali, and M. Rizwan. “Simultaneous reconfiguration, optimal placement of DSTATCOM, and photovoltaic array in a distribution system based on fuzzyACO approach.” IEEE Transactions on SustainableEnergy 6, no. 1. (2015):210–218.

6

Trends in Microgrid Control Anup Kumar Nanda, Babita Panda, Chinmoy Kumar Panigrahi, Arjyadhara Pradhan, and Naeem Hannoon

CONTENTS 6.1 Introduction................................................................................................... 119 6.2 Microgrids and the Indian Market................................................................. 121 6.3 Problems in Islanded Mode of Operation...................................................... 121 6.3.1 Complicated Control.......................................................................... 121 6.3.2 Energy Storage................................................................................... 121 6.3.3 Sustainable Advancement.................................................................. 122 6.3.4 Fault Detection................................................................................... 122 6.3.5 Voltage and Power Balance............................................................... 122 6.3.6 Economic Dispatch............................................................................ 122 6.4 Features of Microgrid Control Systems......................................................... 123 6.5 Control Strategies for Microgrids.................................................................. 124 6.5.1 Droop Control.................................................................................... 125 6.5.1.1 V/f Control.......................................................................... 125 6.5.1.2 PQ Control.......................................................................... 125 6.5.2 Master-Slave Strategy........................................................................ 126 6.6 Control Policies in a Microgrid..................................................................... 127 6.6.1 Centralized Control Scheme.............................................................. 127 6.6.1.1 Grid-Level Control.............................................................. 127 6.6.1.2 Central Processing Control................................................. 127 6.6.1.3 Field-Level Control............................................................. 127 6.6.2 Decentralized Control Scheme.......................................................... 128 6.7  Challenges and Tools in Microgrid Power Quality........................................ 130 6.8 Conclusion..................................................................................................... 132 References............................................................................................................... 132

6.1 INTRODUCTION There still exist many areas which are not feasible to access from conventional main grids. In such areas where uninterrupted power supply becomes a challenge, the evolution of small autonomous grids stands out as the best complement. The recent DOI: 10.1201/9781003121626-6119

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revolutionary changes in power systems allowing renewable energy sources (RESs) to be mingled into the power supply mix have made microgrids more prominent. Microgrids have proven highly beneficial in remote areas where major power challenges persist in terms of transmission, distribution and availability. Currently, the depletion of traditional conventional power sources has already reached extremes of risk. Future generations are likely to be highly affected if new energy sources don’t run posthumously into the power supply system. A  lot of research has been suggested in the literature encompassing control techniques in microgrids. Since renewable energy sources have a tendency to deliver intermittent power, control of microgrids is a crucial task. The major responsibilities of a microgrid controller comprise voltage-frequency regulation, active-reactive power exchange, hassle-free transition of modes between grid-connected and islanded, uninterrupted supply to critical loads and so on. Islanded operation of microgrids, which are DC, is advantageous over grid-tied systems in terms of voltage frequency synchronization issues. Microgrids are an integral part of a carbon-free economy and reliable power systems. Different renewable energy sources can be integrated easily and efficiently with the normal AC as well as DC loads. The motivation behind this chapter is to chart the relevant research done in the case of control of microgrids. A schematic diagram of a microgrid connected to loads is shown in Figure 6.1. Although different strategies have been deployed to promote microgrid and smart grid technology, many unanswered challenges still restrict the seamless growth of this technology. This chapter focuses on the existing challenges and trends in microgrid control and protection. The rest of this chapter is organized as follows. Section 6.2 briefly summarizes the state of microgrids in Indian markets. In Section 6.3, various problems related to islanded operation of microgrids are discussed. Sections6.4 and 6.5 focus on the

FIGURE 6.1  Microgrid connected to loads.

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requisites and techniques of microgrid control systems. Section 6.6 discusses hierarchical control architecture for microgrids. In Section  6.7, some major persisting challenges in microgrid control are presented.

6.2 MICROGRIDS AND THE INDIAN MARKET India has the largest network of integrated power in the world. Also, the ever-growing population raises a pertinent question about conventional power systems in terms of load management and uninterrupted power supply. The literature reveals an average loss as high as 26% associated with conventional systems. On this front, microgrids can be a feasible alternative pertaining to economic electrification over widespread regions. Keeping in view the per capita income and paying capability of Indian consumers, microgrids prove a comfortable option, ensuring economic and uninterrupted power.

6.3 PROBLEMS IN ISLANDED MODE OF OPERATION In islanded state, the microgrid works separately without any connection with the power grid. So in order to provide persistent supply to loads, in this mode, control and protection risks are excessive. The demand may alter at times when the islanded mode of the microgrid is at risk. So proper load-sharing schedules should be accordingly implemented to avoid any cutoff in power supply, while grid-connected mode has a choice of bidirectional power flow between the powergrid and respective microgrid [1,2,3]. Therefore demand management can be done adequately with respect to the power grid in grid-connected operation. But when the power grid fails, major threats have also come up in order to provide consistent supply per demand as well as control of the integration of various renewable energy systems connected to the grid and from the protection point of view of the microgrid.

6.3.1 Complicated Control When a large number of microgrids are connected as supply systems to the distribution grid, control becomes critical. Since microgrids are interfaced with a plethora of renewable energy sources and they are not connected to the host grid, control becomes more critical. So different microgrids need to be controlled by different DER units [4]. Integration of DERs in the system helps in reducing the physical and electrical distance between generation and loads. A local control approach based on the local measurements at the microgrid and a central control approach based on the cumulative and simultaneous function of several microgrids can be considered precisely to reach the power-demand balance [5]. Appropriate load dispatch schemes should be pre-decided, and loads should be supplied accordingly.

6.3.2 Energy Storage Popularly, microgrids are modeled with solar PV energy or wind energy. But the major drawback of supply systems is the intermittent nature of power generation. Since the systems are dependent on the environment and weather conditions, there is no

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guarantee of a stable supply of power. During system disturbances, there is a major impact on the distributed energy storage systems interfaced with the microgrids. The response and health of these energy storage systems need to be studied carefully to stabilize the voltage-frequency and demand-generation. The literature suggests that a distributed energy storage system can cater to the consequences of the fluctuating supply of renewable energy sources [6, 7, 8, 9].

6.3.3 Sustainable Advancement When the economics of microgrids is taken into account combining several costdetermining factors such as fuel expenses, maintenance costs, running capital, labor costs and so on, it can be supposed that energy generation from renewable sources is economic and more cost effective than traditional energy generation processes. The introduction of power electronic devices has added to the reduction of hardware and manpower as well, resulting in a reduction of overall cost of energy generation as compared to conventional power systems. If microgrids are planned and managed properly, then it can lead to substantial development and sustainable advancement of microgrid technology in the near future [10, 11, 12].

6.3.4 Fault Detection As a variety of energy sources are interfaced with microgrids, the operation of different microgrids is different. In the case of any system faults, the traditional detection system fails to locate the fault exactly. In conventional power systems, relay technology is used for the purpose. But for microgrids, advanced and new detection techniques need to be effectively employed to mitigate the faults in the system without affecting the connected loads [13].

6.3.5 Voltage and Power Balance Since different energy sources generate electricity at different levels of voltage, an eye must be kept on the frequency of the output voltage and power of the microgrids. Also, the microgrid must be able to handle any excess or shortfall of power without any reference to the host grid. Outputs of individual units must be traced and matched with the proper reference values to keep the deviations within safe permissible limits [14].

6.3.6 Economic Dispatch A microgrid can be made to feed either purely AC or purely DC loads. Schematic diagrams of a typical DC microgrid and AC microgrid are shown in Figures 6.2 and 6.3, respectively. Scheduling the microgrid optimally to dispatch and manage connected loads and corresponding demands helps to reduce operation cost greatly. In islanded mode, reliability conditions need to be taken care of seriously and critically. Load-sharing schedules can be used in conjugation with optimal load management strategies to grow profit [15].

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FIGURE 6.2  Schematic representation of DC microgrid.

FIGURE 6.3  Schematic representation of AC microgrid.

6.4 FEATURES OF MICROGRID CONTROL SYSTEMS For safe and steady operation of an islanded microgrid while facing many challenges, some of the desirable features of the control system are as follows. • Output Control: The output voltage and currents of various distributed generations should be followed accurately to the nominal reference values. Prompt actions must be taken in the case of any mismatch found in the system [16]. • Power Control: A  microgrid must adopt techniques to provide balance between the active and reactive power in the system. Individual control of

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FIGURE 6.4  Operating modes of microgrids.

power is required at times of extra generation or power deficit at different DERs without altering the equilibrium of the system [17]. • Lossless Distribution Networks: Distribution of microgrid power according to the demand of loads is an essential requirement. However, in isolated areas where microgrids are the main supply of energy, cheap and lossless distribution is an important aspect of the control architecture. • Autonomous Operation: The control technique must facilitate the microgrid working separately, whether in grid-connected or islanded mode. Autonomous action allows the microgrids to switch between various operational modes as well as a choice of convenient operating point [18,19]. • Economic Dispatch: Since microgrids supply power to remote areas, it is a important requirement to maintain the loads efficiently. A prior load management plan including a prompt response to demand absolutely decreases the operating and running charges in the microgrid. Moreover, economic dispatch aids the system in the long run by largely decreasing maintenance prices.

6.5 CONTROL STRATEGIES FOR MICROGRIDS Renewable energy sources have evolved as a faithful alternative to fast-depleting conventional fuels. Due to the abundance of availability and their eco-friendliness, the integration of RES with microgrids has made strides in the era of ever-growing

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electricity demands. The traditional power flow techniques involve a unidirectional flow of power. But this mechanism is not suitable for the power flow associated with microgrids. In standalone mode, the slack bus is not taken into account [20,21,22]. Since the power obtained from RES-enabled microgrids is intermittent in nature, the grid frequency is taken to be a variable.

6.5.1 Droop Control Droop control technique tracks the droop characteristics of the generation unit. At times of inadequate generation, droop control effectively adjusts the shading of the microgrid as required. The conventional P-ω and Q-V droop techniques have several disadvantages, as follows. • As the load dynamics are not taken into account, this may lead to sudden power cutoff in the case of demand increase or decrease. • The uses of power electronic devices introduce harmonics to the output of the microgrids so traditional schemes fail to manage non-linear loads. • The basic regulation of any control scheme is to establish an overall system frequency to which the connected loads report. But traditional methods fail to establish the system frequency with dynamic loading conditions. However, the new droop control manifesto is preferable over traditional schemes in terms of enhanced flexibility and prompt response to any disturbance. The lion’s share of the control scheme depends on local measurements at the microgrids. This wipes out the requirement for a complex communication architecture, and there is hardly any interdependency among the local controllers. For islanded mode of microgrids, in the literature, two types of droop control schemes have been suggested [23,24,25]. 6.5.1.1  V/f Control The voltage generated by microgrids is stochastic in nature. Also, due to the presence of power electronic architecture in the system, harmonics appear at every instance. The grid frequency is taken as a variable in this method. A particular reference is set by the control mechanism for output voltage of the distributed generations and frequency of the grid. Due to mismatch in voltage level and frequency, oscillations occur in the system. The magnitude of oscillations directly affects the capacitance of the voltage control oscillator. Fluctuations in capacitance values of the voltagecontrolled oscillator result in variable frequency. Droop characteristics of the load determine the amplitude of fluctuations. Voltage control ensures the amplitude of the voltage at a particular level by adjusting the reactive power output of the distributed generations. The frequency control adjusts the active power output of the distributed generations [26,27,28]. 6.5.1.2 PQ Control The microgrid central controller sets a nominal value as reference for the active and reactive power generated by the microsource. This method ensures a constant active and reactive power output of the microsource. When the microgrid operates in

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FIGURE 6.5  Droop characteristics of distributed generation.

FIGURE 6.6  V/f control: (a) voltage control, (b) frequency control.

islanded mode, this control technique is deployed for distributed power supply, where a constant power level is maintained at the time of dispatch. In standalone operations, the distributed power system is taken as the main control unit. The PQ control technique is applied, followed by V/f control [13,29,32].

6.5.2  Master-Slave Strategy In islanded operation of microgrids, the distributed power is considered the master distributed generation. The strategy for output voltage and frequency controls the other distributed generations interfaced with the master. Usually the master distributed

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generation carries the main power. The controlled coordinated power flows through all other distributed generations. This strategy employs voltage-frequency control followed by active-reactive power control for other distributed generations [36, 37].

6.6 CONTROL POLICIES IN A MICROGRID Generally, two approaches are tried with microgrids to achieve faithful and reliable control: centralized and decentralized. A centralized control scheme operates on global data of different distributed generations accumulated centrally at a control processing unit. This scheme requires an extensive communication network to collect the instantaneous data and employ the optimal control commands. The communication network replicates the human sensory system. The central cluster behaves as the brain, which gathers all data from different controllers simultaneously and consistently through the nerves and sensory neurons. Then it is on the part of the brain of the network to process the information and appropriately decide the control variables and their corresponding control signals for each isolated controller [18, 22, 25, 26]. Contrary to this, the decentralized control method depends upon local data collected at each distributed generation to control the unit. Each component operates autonomously, and close coordination between the individual components is necessary. A  decentralized system is unaware of the changing of control variables.

6.6.1 Centralized Control Scheme The literature suggests a three-tier policy for the centralized control scheme: gridlevel, central processing-level and field-level control. 6.6.1.1  Grid-Level Control This level decides the energy dispatch policy for the microgrid according to market economic stands. The energy generation schedule and distribution schedule are stacked in this level. The optimal balance of active reactive power is preset at this stage before opening up to distribution. 6.6.1.2 Central Processing Control This stage is the hub for all the local data received from distributed generations. A microgrid central controller (MGCC) matches the generated voltage level and frequency with the preset reference values. Accordingly, an error signal is advanced to the generation modules to satisfy the required levels of active and reactive power in the system. Decisions in emergency situations regarding load shedding, blackouts or black restoration are identified in this level of control [28, 30,38,39]. 6.6.1.3 Field-Level Control Each distributed generation unit is provided with a local controller which independently operates the control variables based upon local measurements and calculations. This level is more reliable, as it does not involve a communication network. Droop control is adopted to manipulate the control variables [31, 33].

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FIGURE 6.7  Centralized control policy for microgrids.

6.6.2 Decentralized Control Scheme This scheme doesn’t involve a central processing unit. Rather each module has its own local controller. Different control variables are addressed for different units. No common control command flows over the system. This method is more popular in islanded operation of microgrids, as it does not require a communication network. The distribution network is classified as small individual units. Each unit operates autonomously and adheres to optimal operating conditions. This control requires fewer observed variables and acts efficiently by observing fewer parameters to issue control commands to the corresponding unit. All the decisions made by the controller are communicated, irrespective of systemwide changes. Power systems extending over larger areas are hard to handle with decentralized control, as the distance between the components increases the number of messages communicated between the controller and system, thereby requiring a dedicated communication network. Also, the coupling between active and reactive power in the system leads to problems. So a midway policy is adopted to address such situations. The midway policy involves three stages of control: primary, secondary and tertiary. These levels are distinguished from each other in terms of speed of response, communication requirements and reference set values [34, 35]. A general control hierarchy is shown in the following figures.

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FIGURE  6.8  Control strategies: (a) centralized, (b) decentralized, (c) distributed, (d) hierarchical.

FIGURE 6.9  Hierarchical control in microgrids.

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FIGURE 6.10  Multilevel control of microgrids.

The main functions of the multilevel control techniques are as follows. • Primary level targets local measurement of different control variables. Local voltage and current control is attained in this level [40, 43]. • Secondary control gets information from primary control. It focuses on power quality control, coordination among different sub-modules, power flow control and so on. This level refers to the microgrid energy management system (MEMS). This level is responsible for the optimal dispatch and economic working of the microgrid [41, 44, 46]. • Tertiary control is the top control stage, which directs the microgrid as a whole. This control level establishes long-term set points known as the optimum operating range for the whole microgrid unit. It takes care of the total active and reactive power balance [47, 48].

6.7  CHALLENGES AND TOOLS IN MICROGRID POWER QUALITY Maintaining the quality of power delivered to the loads is as important as generation. The IEEE has set certain standards for the quality of power associated with microgrids. Moreover, the imbalance in load demand creates challenges for

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FIGURE 6.11  Control architecture of microgrids.

microgrids, especially in islanded mode of operation [42, 45]. Some of the major challenges are given in the following: • Integration of renewables. • Integration of electric vehicles. • Use of power electronic components with a limit on the harmonics injected into the system. Different tools to satisfy standardized power quality are as follows: • • • • • • • • • •

Modern monitoring devices Smart metering facilities Smart appliances Storage devices Advanced information and communication technology Computational intelligence Advanced control techniques Efficient demand management Active demand response Economic load dispatch schedules

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6.8 CONCLUSION In recent years, microgrids have evolved as a hot area of research. In comparison to conventional wide-area power systems, the control variables in microgrids are available in plenty. In parts of the United States and Europe, research is at a peak to make microgrids more stable, economic and environmentally friendly. For remote electrification, microgrids are more reliable and steady because they have fewer observed parameters for each control scheme. The development of microgrids has geared up because of projects like China’s 863 and 973 development projects, and they stand as an alternative to traditional power systems. The advancement of microgrids has driven the demand for implementation of various renewable energy resources like wind, PVs and battery and fuel cells into mainstream power scenarios. For conventional microgrids, droop control is determined to be the universally used control technique, while for islanded mode, voltage frequency control is widely used to control microgrids. A hierarchical control scheme is noticeable in the literature, playing an important role in control of islanded microgrids by breaking down the whole system into small modules. The most general control strategy consists of two stages, centralized and decentralized control. The PQ control method focuses on power coupling problems in islanded microgrids. Other control strategies include master-slave strategy and peer-to-peer control strategy for islanded microgrids; V/f and PQ control methods are discussed where study is limited. This chapter presents a cumulative overview of various control trends for microgrids. Future research in the field can lead the process of transforming conventional power systems into smart power systems. Ongoing research has reached the halfway pointin the realization of smart grids as an alternative to traditional power systems.

REFERENCES 1. F. Mumtaz, M. H. Syed and M. Al Hosani, et al., “A simple and accurate approach to solve the power flow for balanced islanded microgrids,” in IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC), pp. 1852–1856, 2015. 2. J. Lopes, C. Moreira and A. Madureira, “Defining control strategies for microgrids islanded operation,” IEEE Transactions on Power Systems, vol. 21, pp.  916–924, May 2016. 3. IEEE guide for design, operation, and integration of distributed resource island systems with electric power systems, IEEE Std. 1547.42011, pp. 1–54, 2011. 4. T. Dragičević, X. Lu, J. C. Vasquez, et al., “DC microgrids—part I: A review of control strategies and stabilization techniques,” IEEE Transactions on Power Electronics, vol. 31, no. 7, pp. 4876–4891, July 2016. 5. T. Dragičević, X. Lu, J. C. Vasquez, et al., “DC microgrids—part II: A review of power architectures, applications, and standardization issues,” IEEE Transactions on Power Electronics, vol. 31, no. 5, pp. 3528–3549, May 2016. 6. R. Zamora and A. K. Srivastava, “Controls for microgrids with storage: Review, challenges, and research needs,” Renewable and Sustainable Energy Reviews, vol. 14, no. 7, pp. 2009–2018, September 2010. 7. R. Zamora and A. K. Srivastava, “Controls for microgrids with storage: Review, challenges, and research needs,” Renewable and Sustainable Energy Reviews, vol. 14, no. 7, pp. 2009–2018, September 2010.

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8. T. Dragičević, X. Lu, J. C. Vasquez, et al., “Tertiary and secondary control levels for efficiency optimization and system damping in droop controlled DC–DC converters,” IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 2615–2626, November 2015. 9. A. Bidram and A. Davoudi, “Hierarchical structure of micro grids control system,” IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1963–1976, December 2012. 10. W. Chengshan and L. Peng, “Development and challenges of distributed generation, the micro-grid and smart distribution system[J],” Automation of Electric Power Systems, vol. 34, no. 2, pp. 10–14, 2010. 11. W. Guo-dong, “Overview on smart micro-grid research [J],” China Electric Power (Technology Edition), vol. 2, p. 021, 2012. 12. T. Caldognetto, S. Buso, P. Tenti, et al., “Power-based control of low-voltage microgrids,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 3, no. 4, pp. 1056–1066, 2015. 13. D. I. Brandao, T. Caldognetto, F. P. Marafao, et al., “Centralized control of distributed single-phase inverters arbitrarily connected to three-phase four-wire microgrids,” IEEE Transactions on Smart Grid, vol. 8, no. 1, pp. 437–446, 2017. 14. A. Micallef, M. Apap, C. Spiteri-Staines, et al., “Single-phase microgrid with seamless transition capabilities between modes of operation,” IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 2736–2745, 2015. 15. A. Milczarek, M. Malinowski and J. M. Guerrero, “Reactive power management in islanded microgrid—proportional power sharing in hierarchical droop control,” IEEE Transactions on Smart Grid, vol. 6, no. 4, pp. 1631–1638, 2015. 16. X. Chen, Y. Hou, S. C. Tan, et  al., “Mitigating voltage and frequency fluctuation in microgrids using electric springs,” IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 508–515, 2015. 17. Z. Akhtar, B. Chaudhuri, S. Y. Ron Hui, et al., “Primary frequency control contribution from smart loads using reactive compensation,” IEEE Transactions on Smart Grid, vol. 6, no. 5, pp. 2356–2365, 2015. 18. T. L. Vandoorn, J. C. Vasquez, J. De Kooning, et al., “Microgrids: Hierarchical control and an overview of the control and reserve management strategies,” IEEE Industrial Electronics Magazine, vol. 7, no. 4, pp. 42–55, 2013. 19. H. Han, X. Hou, J. Yang, et al., “Review of power sharing control strategies for islanding operation of AC microgrids,” IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 200–215, 2016. 20. L. Meng, E. R. Sanseverino, A. Luna, et  al., “Microgrid supervisory controllers and energy management systems: a literature review,” Renewable and Sustainable Energy Reviews, vol. 60, pp. 1263–1273, 2016. 21. Y. Han, H. Li, P. Shen, et al., “Review of active and reactive power sharing strategies in hierarchical controlled microgrids,” IEEE Transactions on Power Electronics, vol. 32, no. 3, pp. 2427–2451, 2017. 22. M. Yazdanian and A. Mehrizi-Sani, “Distributed control techniques in microgrids,” IEEE Transactions on Smart Grid, vol. 5, no. 6, pp. 2901–2909, 2014. 23. A. Milczarek, M. Malinowski and J. M. Guerrero, “Reactive power management in Islanded microgrid—proportional power sharing in hierarchical droop control,” IEEE Transactions on Smart Grid, vol. 6, no. 4, pp. 1631–1638, 2015. 24. H. Mahmood, D. Michaelson and J. Jiang, “Reactive power sharing in islanded microgrids using adaptive voltage droop control,” IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 3052–3060, 2015. 25. A. Bidram and A. Davoudi, “Hierarchical structure of microgrids control system,” IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1963–1976, December 2012. 26. Z. Lecai, “Technology development research of distributed energy and the micro-grid [J],” Journal of Shanghai Electric Technology, vol. 1, p. 013, 2013.

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27. J. M. Guerrero, P. C. Loh and T. L. Lee, et  al., “Advanced control architectures for intelligent microgrids—part II: Power quality, energy storage, and AC/DC microgrids,” IEEE Transactions on Industrial Electronics, vol. 60, no. 4, pp. 1263–1270, 2013. 28. Thillainathan Logenthiran, Ramasamy Thaiyal Naayagi, Wai Lok Woo, Van-Tung Phan and Khalid Abidi, “Intelligent control system for microgrids using multiagent system,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 3, no. 4, 2015. 29. A. Mehrizi-Sani and M. R. Iravani, “Potential-function based control of a microgrid in islanded and grid-connected modes,” IEEE Transactions on Power Systems, vol. PP, no. 99, pp. 1–8, 2010. 30. J. Y. Kim, et  al., “Cooperative control strategy of energy storage system and microsources for stabilizing the microgrid during islanded operation,” IEEE Transactions on Power Electronics, vol. 25, no. 12, pp. 3037–3048, December 2010. 31. Z. Fan, et al., “Smart grid communications: Overview of research challenges, solutions, and standardization activities,” IEEE Communications Surveys and Tutorials, vol. 15, no. 1, pp. 21–38, 1sr Quart. 2012. 32. S. M. Amin, “For the good of the grid,” IEEE Power & Energy Magazine, vol. 6, no. 6, pp. 45–89, 2008. 33. N. Hatziargyriou, H. Asano, R. Iravani and C. Marnay, “Microgrids,” IEEE Power & Energy Magazine, vol. 5, no. 4, pp. 78–94, 2007. 34. F. Katiraei, R. Iravani, N. Hatziargyriou and A. Dimeas, “Microgrids management,” IEEE Power & Energy Magazine, vol. 6, no. 3, pp. 54–65, 2008. 35. A. Mehrizi-Sani and M. R. Iravani, “Potential-function based control of a microgrid in islanded and grid-connected modes,” IEEE Transactions on Power Systems, vol. PP, no. 99, pp. 1–8, 2010. 36. W. Guo-dong, “Overview on smart micro-grid research,” China Electric Power (Technology Edition), vol. 2, p. 021, 2012. 37. P. Kundur, Power System Stability and Control, McGraw-Hill, New York, 1994. 38. R. Zamora and A. K. Srivastava, “Controls for microgrids with storage: Review, challenges, and research needs,” Renewable and Sustainable Energy Reviews, vol. 14, no. 7, pp. 2009–2018, September 2010. 39. S. Bahramirad and W. Reder, “Islanding applications of energy storage system,” in Power and Energy Society General Meeting, 2012 IEEE, pp. 1–5, 2012. 40. T. Xisheng and Q. Zhiping, “Energy storage control in renewable energy based microgrid,” in Power and Energy Society General Meeting, IEEE, pp. 1–6, 2012. 41. T. L. Vandoorn, B. Renders, L. Degroote, et. al., “Active load control in islanded microgrids based on the grid voltage,” IEEE Transactions on Smart Grid, vol. 2, pp. 139–151, 2011. 42. T. L. Vandoorn, B. Meersman, L. Degroote, et. al., “A  control strategy for islanded microgrids with DC-link voltage control,” IEEE Transactions on Power Delivery, vol. 26, pp. 703–713, 2011. 43. M. Pipattanasomporn, H. Feroze and S. Rahman, “Securing critical loads in a PV-based microgrid with a multi-agent system,” Renewable Energy, vol. 39, pp. 166–174, 3// 2012. 44. T. Hiyama, T. Nagata and T. Funabashi, “Multi-agent based operation and control of isolated power system with dispersed power sources including new energy storage device,” in Transmission and Distribution Conference and Exhibition 2002: Asia Pacific, IEEE/ PES, 2002. 45. Y. J. Reddy, S. Dash, A. Ramsesh, et. al., “Monitoring and control of real time simulated microgrid with renewable energy sources,” in Power India Conference, 2012 IEEE Fifth, pp. 1–6, 2012.

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46. S. Bahramirad and W. Reder, “Islanding applications of energy storage system,” in Power and Energy Society General Meeting, 2012 IEEE, pp. 1–5, 2012. 47. P. Basak, A. K. Saha, S. Chowdhury, et. al., “Microgrid: Control techniques and modeling,” in Universities Power Engineering Conference (UPEC), 2009 Proceedings of the 44th International, pp. 1–5, 2009. 48. T. L. Vandoorn, B. Meersman, L. Degroote, et. al., “A  control strategy for islanded microgrids with DC-link voltage control,” IEEE Transactions on Power Delivery, vol. 26, pp. 703–713, 2011.

7

Utility Tariff Variation and Demand Response Events A Case Study of Microgrid Design and Analysis Aashish Kumar Bohre and Parimal Acharjee

CONTENTS 7.1 Introduction................................................................................................... 138 7.2 Solar Power Resource Assessment for Case Study....................................... 140 7.3 Modeling of Microgrid System..................................................................... 140 7.3.1 Solar-PV............................................................................................. 141 7.3.2 Battery Storage System...................................................................... 142 7.3.3 System Converter............................................................................... 142 7.3.4 Diesel Generator................................................................................ 143 7.3.5 Load................................................................................................... 143 7.4 Description of Problem and Methodology.................................................... 144 7.4.1 Reliability of System......................................................................... 147 7.4.2 Resilience of System.......................................................................... 148 7.4.3 Demand Response............................................................................. 148 7.4.4 Total Net Present Cost....................................................................... 148 7.4.5 Levelized Cost of Energy.................................................................. 149 7.4.6 Simple Payback.................................................................................. 149 7.4.7 Pollutant Emissions and Particulate Matter...................................... 149 7.5 Results and Discussion.................................................................................. 150 7.5.1 Base Case System (Current Existing System of NITD): Diesel Generator Connected to DVC-NITD-Grid........................................ 150 7.5.2 Proposed Microgrid System: Solar PV-Battery Storage-Diesel Generator Connected to Main Utility Grid....................................... 159 7.6 Conclusions.................................................................................................... 177 7.7 Acknowledgments.......................................................................................... 178 References............................................................................................................... 178

DOI: 10.1201/9781003121626-7137

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7.1 INTRODUCTION Nowadays, renewable energy sources are more auspicious in power generation to develop a more efficient and reliable microgrid system based on the renewable energy system due to the exhaustible nature of fossil fuels and global warming. One of the most abundant renewable sources available in nature is the solar PV. Therefore, the combination of solar PVs with energy storage systems, including generators as power backup-based microgrids connected to the main grid, has great potential to provide better-quality and more reliable power to customers than a conventional system based on a single utility. At present, the extraction of renewable energy sources for power generation systems has gained more attention worldwide. Microgrids may be standalone, an off-grid type or a grid-connected type, and they are self-sustainable energy systems and can maintain system reliability and resiliency. The standalone, off-grid or isolated microgrid requires sufficient storage and a power backup system to maintain reliability. The grid-connected microgrid has the capability to supply power to local loads as well as to the grid by employing net metering or net purchased power from the grid approach. Paterakis et al. [1] presented an overview of a demand response (DR) program while utilizing renewable energy sources connected to the utility and also gave information about incentive and price-based DR event programs to accomplish various economic, technical and operational benefits. Chai et al. [2] analyzed a demand response model to maximize the benefits of power retailers based on incentive rates or rebates when the behavior of customer load demand/consumptions changes. The more cost-efficient renewable hybrid microgrid system is achieved by employing solar and wind energy sources, and optimal incentive charges are attained based on the DR model. He et al. [3] implemented a renewable energy-based microgrid system for sustainable operation of a large-scale residential community in China for grid-connected and standalone mode operation of a microgrid using HOMER to obtain economic performance of various system components and cost-effective design among different configurations. Chrysopoulos et  al. [4] applied cost-effective demand response for smallscale consumers based on prediction of the customers’ individual activities. The planned prototype was also applied to forecast the load demand through which pricing policies can be modified based on customer comfort and partialities. An emergency demand response based on the reliability optimization method by considering generation failure was executed by Aghaei et al. [5], which positively supported system reliability and other technical and economic facilities for emergency operation. Tang et al. [6] employed a load control scheme for demand response used to manage power within the smart grid by performing data center load management with DR  through a data center and smart grid application. Babar et  al. [7] performed an incentive bid-based demand reduction algorithm by direct load control in combined demand response plans which minimize the congestion of the network to decrease the available generation and load demand mismatch. Masrur et al. [8] proposed a techno-economic-environmental analysis of an isolated microgrid system in Bangladesh Island, which concluded that the proposed hybrid microgrid system based on solar PV and wind was efficient to reduce the high per-unit cost of energy and increase the economic and technical feasibility of microgrids. Safamehr

Utility Tariff Variation and Demand Response Events

139

et  al. [9] realized microgrid performances based on cost effectiveness and sustainable energy management with an intelligent demand response approach using an artificial bee colony and quasi-static algorithm to minimize the total cost of energy of the microgrid system. Different configuration planning for microgrids and techno-economic analysis consisting of a PV and wind system for rural-area electrification of parts of Indian states were presented using a different strategy by Suresh et al. [10] and Mazzola et al. [11]. The optimal design and analysis of a nano-grid system based on a solar PV-wind-battery considering laboratory experimentation was given by Tudu et al. [12], which provides an efficient microgrid planning path for practical systems. Zhang et  al. [13] scrutinized the optimal design of CHP-based microgrids, including renewable distributed energy resources like solar, using a multiobjective optimization approach to decrease the cost and environmental impacts. A feasibility analysis of a standalone hybrid microgrid comprising PV, wind and other system components to find the optimal configuration of the microgrid and operation with respect to total cost and the number of energy generation sources was explored in Kumar et al. [14] and García et al. [15]. Thomas et al. [16] utilized different renewable sources with penetration impact for a small isolated hybrid microgrid system design by considering the different scenarios of a case study, and techno-economic analysis was performed for the case study. Sawle et al. [17] evaluated the modeling and analysis of a hybrid off-grid system based on renewable sources like solar and wind to investigate various social, technical and economic criteria such as loss of power supply probability (LPSP), cost economy, particulate matter (PM), emissions and human development using a particle swarm optimization technique for an off-grid system. Guo et al. [18] planned a standalone microgrid system for remote areas considering new multiple objectives for a distribution system using a pareto optimal solution-based approach to obtain different benefits from the planned system. An assessment of numerous potential benefits and motivations offered by hybrid renewable energy sources was given by Elavarasan et al. [19], which argued for more use of renewable sources globally. Recent development in the socio-economic area, motivations, issues, system configuration and planning methodologies using different objectives to evaluate the performance and operation of a hybrid renewable energy system in off-grid and grid-connected modes were analyzed by Babatunde et  al. [20] using different optimization techniques and methodologies and also including the HOMER [21] simulation software platform. The planning and design of low-cost renewable energy solutions for various systems and microgrids based on the availability of renewable energy resources, reliability and sensitivity analysis to decide possible configurations of a system and its analysis were presented by Go et al. [22]. A multi-objective approach for optimal design and analysis of hybrid renewable energy systems with different system components like fuel cell-based micro-CHPs, electrolyzer PVs and wind systems using sunflower optimization was executed by Fan et  al. [23]. Bohre et  al. [24] investigated a multi-objective approach with system performance indices for the optimal planning of distribution systems with different types of distributed generations, including renewables, using optimization techniques. An experimental study

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to manage energy within standalone and grid-connected solar-wind based hybrid systems together with battery storage was implemented by Dali et al. [25] using a controlled interconnection concept of the presented grid. Saib et al. [26] considered a grid-connected hybrid renewable system comprising PV, wind and battery storage simulated in the MATLAB environment to access system performance information such as voltage profile, power injected to the grid and so on. The optimum planning and analysis of a grid-connected hybrid solar-wind-battery-based generation system to meet the optimal generation-demand basis of generic methodology was examined by Khalid et al. [27]. The previous summary and discussion of various microgrid case studies and hybrid renewable energy system design and optimization in different operating modes such as off-grid/standalone/isolated and grid-connected systems motivated using the benefits of a solar-based renewable microgrid system for the proposed case study. Many design and analysis tools and optimization algorithms are available, but the HOMER software tool and MATLAB are widely used and globally accepted by researchers. Hence, both HOMER-grid and MATLAB are utilized for the design and analysis of the proposed case study of National Institute of Technology (NIT), Durgapur. The detailed technical, economic and social benefits of the proposed grid-connected microgrid are presented using the HOMER-grid optimizer, including demand response and utility tariff options. The comparison is given with respect to the existing base case system.

7.2  SOLAR POWER RESOURCE ASSESSMENT FOR CASE STUDY The availability of renewable solar resources for the proposed case study location, NIT Durgapur (NITD)was assessed by NASA’s prediction of the Worldwide Energy Resource (Power) database with HOMER-grid. The details of the proposed case study location, as shown in Figure 7.1 for NITD, are as follows [21]: • Location: Academic Building, NIT, A-Zone, Durgapur, West Bengal 713209, India • Latitude: 23°33.3’ N • Longitude: 87°17.4’ E • Time zone: Asia/Kolkata The topographical location and availability of annual solar data for the case study NITD are shown in Figures 7.1 and 7.2, respectively. The annual average solar global horizontal irradiance is 4.824 kW/m2/day, which is given in Table  7.1. The available solar resources for the NITD case study are updated in HOMER-grid with the help of the MATLAB file interface. The final solar output power is calculated using HOMER-grid.

7.3  MODELING OF MICROGRID SYSTEM The microgrid system model includes the PV array, battery storage, system converter and other essential components to maintain the reliability and resilience of the proposed grid-connected microgrid system, which is discussed in this section.

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FIGURE 7.1  NITD topographical location.

TABLE 7.1 Monthly Average Solar Clearance Index Global Horizontal Irradiance, Temperature and Wind Speed Month

Clearance Index

Radiation (kW/m2/day)

Temperature (°C)

Jan

0.609

4.230

16.960

Feb

0.622

5.010

21.130

Mar

0.614

5.770

26.880

Apr

0.595

6.230

31.660

May

0.555

6.120

32.830

Jun

0.446

4.980

31.010

Jul

0.394

4.350

28.750

Aug

0.400

4.230

28.120

Sep

0.429

4.140

27.200

Oct

0.530

4.430

25.040

Nov

0.601

4.290

21.070

Dec

0.624

4.110

17.300

Annual Average

0.535

4.824

25.663

7.3.1 Solar-PV The solar-PV power output depends on the temperature and irradiation of the study area, which is determined by the following equation [10–12].  I t   r  0 t STC t PPV = PPV × d f ×  STC  × 1 + τc T − T  I   r

{

(

)} (7.1)

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Microgrids

FIGURE 7.2 Availability of annual solar data for NITD.

t 0 where PPV is the PV array power output at instant t; PPV is the PV array capacity for standard test conditions (STC); d is the derating factor of PV array in%; I rt is f the solar radiation at time t in kW/m2; τc is the temperature coefficient; I rSTC is the radiation at STC, that is, 1 kW/m2;T t is temperature at time t in °C; and T STC is the temperature at STC, that is, 25°C.

7.3.2 batteRy stoRage systeM The excess energy generated by microgrid sources during light load conditions is stored in the battery storage system (BSS), and it dispatches suitable energy whenever required. The mathematical equation for battery state of charge during charging and discharging mode is given as [23, 25]: V t × η   t t−1 bat  = BSOC ±  bat BSOC   C bat 

(7.2)

where the +ve and -ve signs are considered for charging and discharging mode operat t−1 tion of the battery energy storage system. Also, BSOC and BSOC are the battery state of charge for the t and t – 1 instances, respectively; η is the efficiency of the batbat t tery;Vbat is the discharged or charged power by the battery at instance t; and C is bat max the battery capacity in kWh. The BES can be charged up to BSOC and discharged up min to BSOC , where the minimum and maximum SOC limits are specified as: min max BSOC ≤ BSOC ≥ BSOC

7.3.3 systeM conVeRteR The electronic system converter is required for maintaining the energy flow among the different AC and DC components. The electric energy is transferred from one

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Utility Tariff Variation and Demand Response Events

element to the other by different operating modes of the system converter, that is, converters (AC to DC)/inverters (AC to DC) per the needed load frequency. The efficiency of the converter can be evaluated as [17]: ηimv =

P

(7.3)

P + P0 + kP 2

where P, P0 and k are correspondingly obtained as: 2

 10  1 1 − P0 − 1, P0 = 1 − 99  − − 9 , k =   η100  η10 η100

and P = Pout / Pn

(7.4)

where η and η are the inverter’s efficiency for 10% and 100% of rated energy, 10 100 respectively.

7.3.4 diesel geneRatoR The diesel generator set is used for alternate backup supply power in the system. The diesel generator fuel consumption is dependent on the size of generator and connected load to which the generator supplies the power. The diesel generator fuel consumption can be calculated as [23]: Fcons = k 0W0 +k1Wdg

(7.5)

where k0 and k1 are fuel consumption coefficients, W0 and Wdg are diesel generator rated capacity and output power. Also, k0 =0.081451 l/kwh and k1= 0.2461 l/kwh. The efficiency of the diesel generator is defined by the ratio of the power output to the fuel consumption heating value [23]. ηdg =

Wdg Fcons ×Vlh

(7.6)

where Vlh is the low heat value of gas/oil and ranges from10 to 11.6 kwh/lit.

7.3.5 load The commercial load is considered in the study on the basis of daily energy consumption in kWh/day of NITD. The estimation of the average load of the microgrid system can be distinguished as the area under the load curve for the specified duration. The per day and annual energy demand of NITD are given in Figures 7.3 and 7.4, respectively. The peak load demand is 1,488.52 kW. The average daily, monthly and annual electric energy consumption/demand/load are 13,830.6 kWh/day, 420.7 MWh/month and 5,048.2 MWh, respectively. The total load of the NITD case study is divided into critical or primary load and non-critical load. The critical load is the primary load which requires a power supply for primary official work and a power supply in emergencies for laboratory, health center and other main priority work, which is supplied even in outages from a backup supply. The load model of the case

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Microgrids

FIGURE 7.3  Load profile for a day at NITD.

FIGURE 7.4  Seasonal load profile of NITD.

study is implemented in HOMER-grid with the help of an imported MATLAB file interface. The parameters considered for proposed grid-connected microgrid case study are given in Table 7.2.

7.4  DESCRIPTION OF PROBLEM AND METHODOLOGY The base case of the presented work is the current existing system of the NITD, WB, India. The current system at NITD, including the AC load, diesel generator (DG) and main grid, is considered the base case system. The NITD system is connected to the main grid, which is connected to the Damodar Valley Corporation (DVC) power plant following the DVC tariff (tariff-1) order for energy consumption [28]. The microgrid comprising solar PV, battery storage and diesel generator is proposed for the NITD

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Utility Tariff Variation and Demand Response Events

TABLE 7.2 Different Components of Proposed Microgrid for Case Study Solar-PV (Generic flat PV) Cost: Capital: ₹ 3000/kW O&M: ₹ 18/yr Replacement: ₹ 3000/kW Lifetime: 25 yr Sizes Considered: 300, 400 and 500kW Derating Factor: 80% Battery Storage (Generic LA) Cost: Capital: ₹ 550/kWh O&M: ₹ 10/yr Replacement: ₹ 550/kWh Lifetime Throughput: 3000 (kWh) Nominal Voltage: 6 V SOC Min.: 20%

Diesel Generator Cost: Initial: ₹ 52,500/kW Replacement: ₹ 35,000/kW O&M: ₹ 0.7 /h Sizes Considered: 1500 kW Maximum Load Ratio: 25% Lifetime: 15,000 hrs Diesel Fuel Price: ₹ 66.0/L Lower Heating Value: 43.2 MJ/kg Density: 820.00 kg/m3 Sulfur Content: 0.4% Carbon Content: 88.0%

System Converter Cost: Capital: ₹ 300/kW O&M: ₹ 10/yr Replacement: ₹ 300/kW Efficiency: 90% Lifetime: 15 yr Rates: Discount: 8% Inflation: 2% Interest: 6% Project Lifetime: 25 yr

system in this study, which offers a lower per-unit price of energy and net present cost. The pollution in Durgapur city is also increasing day by day due to many reasons such as the presence of large industries, use of conventional and fossil fuel-based power plants, internal combustion (IC) engine vehicles and many more. Thus, global warming, human health and other social impacts are adversely affected. The current need is to develop a green campus globally; therefore, a renewable source, solar PV, is proposed with the existing NITD grid-connected power system. The proposed microgrid integration will improve the system’s social, economical and technical stability at a lower per-unit energy price and with flexible and reliable overall system operation. The energy generated by the proposed microgrid system, including PV, is utilized to meet the load demand with grid connection mode. The total load demand is served by the combined economic operation of the grid-connected microgrid including, PV panels, storage systems and DG unit. When the supply by the combined operation of the solar PV and grid is more than load demand, then excess energy is stored in the battery storage systems, which are supplied whenever required to meet the energy deficit. If the battery storage system is unable to meet the load, then the main grid (NITD-DVCgrid) supplies energy to meet the total load demand. The diesel generator is used to meet the total load demand under an outage or emergency condition to maintain reliable and resilient operation of the system. The proposed grid-connected microgrid is implemented with the combined platform of HOMER-grid and MATLAB; the main objective is to minimize the per-unit cost of energy and net present cost. The flow chart of the proposed system design methodology is shown in Figure 7.5. The HOMER-grid software created by the National Renewable Energy Laboratory is employed to design and optimize the grid-connected microgrid solution in the projected case study. HOMER-grid covers the simulation and design with probable

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Microgrids

Start Define study area location and considered system Select and define the load profile of study case system Select the geographical data of solar resource for study location (Irradiation, temperature etc) and fuel price Define the parameters of microgrid system components such as ratings, costs and lifetime etc of PV, battery, converter, diesel generator etc

Include DR events and go back to repeat steps

Perform multicriteria optimization using HOMER-grid Optimization Without DR events and find microgrid solution Obtain the results for cost economy, reliability, resiliency and emission analysis of proposed microgrid without DR Print final report of result for different optimal design configuration of HRES End

FIGURE 7.5  Flow chart for design methodology of proposed microgrid system.

constraints of the system for optimization. HOMER-grid is innovative program design software to construct the new model and its operation for the renewable energy system-based microgrid design with a grid-connected system. The system simulation and analysis fundamentally depend on the choice of system components selected in any study. HOMER-grid allows the development of system components in huge amounts and sizing the for the successful operations of entire system and also evaluates the total amount of solar PV, battery and converter in the study. The optimum dynamic system design is selected during the grid-connected microgrid

Utility Tariff Variation and Demand Response Events

147

planning through HOMER-grid optimization and simulation analysis based on the minimum net present cost and energy per unit cost. The reduction of demand charge and other cost-saving benefits can be achieved by different options for reducing electricity bills. The comparison of different costs and savings for installing different combinations of batteries, solar panels and generators are also presented. HOMER-grid uses powerful optimization to find the system’s minimum cost of energy with a minimum net present cost that will maximize the savings of the system. A bill from an electric utility can be composed of different types of charges. The charge for energy is the total charge for consuming the quantity of energy in kWh for the month. The demand charge is generally the charge for the highest peak power draw in kW or megawatts (MW) for the month. Also, the fixed charge is a charge that is the same every month and is not affected by consumption or peak demand. Here, the optimization tool considers generators as a system for peak shaving and demand charge reduction. The total cost, capital cost, O&M cost, replacement cost, fuel cost and other parameters are also obtained with HOMER-grid for the hybrid system. The demand response program is used by the utility to shift or lessen electricity consumption during peak load hours, which offers incentives to customers with different time-of-use rates; also, during peak load events, these DR  programs can permit the utility to avoid new investment for energy generation. HOMER-grid computes the demand limit first, including the demand charge, which produces a separate demand limit for all demand response events. It then executes optimization for demand response events to minimize overall costs. If multiple demand response events are included for a month, then chronological order is used in the optimization process. Finally, HOMER-grid optimizes all the configurations of the grid-connected microgrid system based on the multicriteria optimization approach based on various economic and technical parameters such minimization of total net present cost, levelized cost of energy, total annualized cost, reliability, emissions, maximization of reliability and resiliency and so on. Therefore, the different technical and economic performance parameters for the proposed grid-connected microgrid are defined as the following.

7.4.1  Reliability of System Reliability is the ability of the system to maintain operations during the outage of the utility for a short time, generally lasting a few hours or less. These outages may occur multiple times in a year. Maintaining operations during longer outages comes under resiliency. Systems with energy storage or backup generators can continue operations under the grid outage condition. In the proposed HRES system, reliability is simulated based on outages that happen 10 times per year on average for a 2-hour average repair time. No electrical energy is purchased or sold by the grid during the outage time steps. Also, the reliability of the system is evaluated by the concept of loss of power supply probability. The LPSP can be defined as the probability of deficient power supply consequences when the renewable hybrid system

148

Microgrids

cannot fulfill the load demand. The LPSP can be evaluated based on the following equation [8]:

∑ LPSP =

T t =0

(

t P × Failure ×Time with P t Supplied < Pdemand

)

Nt

(7.7)

where T is the entire period and Nt is the intervals for deficit energy.

7.4.2 Resilience of System Resilience can be defined as the ability of system for a facility to respond to an extended, multi-day utility outage like a natural disaster such as hurricanes or wildfires. It provides a supplementary value stream by offering a hybrid/generator project’s value in extended outages. In the proposed HRES system, resilience is simulated based on an outage that occurs once in every 1.0 years. The simulated grid shutdown starts on 21 September 03:00 and ends on 23 September 03:00 after 2 days. During the resilience outage duration, the HRES work as stand-alone systems (i.e., disconnected from grid) for resiliency applications. During a resilience event, only the critical/primary electrical load and thermal load are served, not non-critical loads.

7.4.3 Demand Response The demand response model for the proposed study is used during peak load hours to shift or reduce energy consumption, which offers economic incentives. In this case study, the demand reduction incentives are considered to be 35 ₹/kW for all DR events. Demand response programs are incentives offered by utilities to lower consumption at certain times during the year. If the system can manage the facilities to reduce demand during the event successfully, the utility would pay the pre-approved amount for every kW reduced, which is known as the demand reduction incentive. The NIT Durgapur utility (DVC-NITD) tariff offers an incentive of ₹ 35.00 for every kW reduced. So, it’s required to reduce consumption for a period of 3.0 hours when notified by the utility. The start-up for this program leads to total revenue for the proposed grid-connected microgrid system. Six demand response events occur in a year for the proposed case study, as given in the following, during which the revenue is incurred. • • • • • •

Event-1: 15 Mar 03:00 PM Event-2: 16 Apr 02:00 PM Event-3: 21 Sep 04:00 PM Event-4: 08 Oct 03:00 PM Event-5: 27 Nov 02:00 PM Event-6: 24 Dec 01:00 PM

7.4.4 Total Net Present Cost The total net present cost (TNPC) of a system can be defined as the total value of the present system cost components, including initial cost, replacement cost, operation

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Utility Tariff Variation and Demand Response Events

and maintenance cost, emissions penalty, fuel costs and power purchasing costs grid over the project lifetime. The total net present cost of a system is the main economic parameter. The different system configurations are ranked, which is used to estimate the levelized cost of energy and total annualized cost.

7.4.5 leVelized cost of eneRgy The levelized cost of energy (LCOE) is an eminent noteworthy factor to determine cost-effective analysis of a distribution grid or network in the presence of renewable sources. The average cost of energy or LCOE can be defined as [9–10]: LCOE =

C

TNPC H = 8760

∑

×CRF

(7.8)

Pdemand

H =1

where Pdemand is energy consumed (kWh) hourly, CTNPC is total net present cost and capital recovery factor is denoted as CRF, which is determined by the following equation: n

CRF =

r (1 + r ) n

(1 + r )

(7.9)

−1

where r is the actual rate of interest, and n is system/project lifetime.

7.4.6 siMple payback Simple payback is the number of years in which cumulative cash flow changes move towards positive values from the proposed system’s negative values relative to system base case. The payback indicates how much time it will take to recover the investment difference costs between the base case system and the proposed system.

7.4.7 pollutant eMissions and paRticulate MatteR Pollutant emissions (CO2, NOx, SO2, etc.) and particulate matter are taken into consideration as social factors. The grid is majorly supplied with thermal power plants; therefore, generally the pollutant emission component consists of CO (carbon monoxide), CO2 (carbon dioxide), SO2 (sulfur dioxide), NOx (nitrogen oxides) and particulate matter. These pollutant emission elements badly affect the environment as well as human health. Mainly, PM affects human health adversely by affecting the respiratory system and other health issues. The equivalent PM consists of M 2.5 (air pollutant particles less than or equal to 2.5 μm diameter) and PM 10 (air pollutant particles less than or equal to 10.0 μm diameter). The pollutant emission factor for CO2 is 632.0 g/kWh, SO2 is 2.74 g/kWh and NOx is 1.34 g/kWh [17]. Also, particulate matter is given on the basis of a linear regression equation with correlation r=0.884 of PM and carbon weight [17]: PM = (0.47 ×Wcarbon ) + 0.12

with correlation r = 0.884

(7.10)

where PM = PM 2.5 + PM 10 , and Wcarbon is the weight of total carbon emissions.

150

Microgrids

7.5 RESULTS AND DISCUSSION The results of the analysis of the proposed microgrid design comprising solar PV, battery storage and a diesel generator connected with the DVC-NITD-grid (main grid utility) by using HOMER-grid multicriteria optimization to fulfill the load demand of the study area is presented with demand response events for the two optional tariffs of the grid utility. The feasible design and sizing of the proposed microgrid and parameter estimation are performed on the basis of minimum net present cost and cost of energy with simultaneous maximization of reliability for available renewable resources in the NITD system case study. The best configurations are analyzed and discussed based on their technical and economic performance benefits using HOMER-grid multicriteria optimization in the design and analysis of various configurations. The optimal design solution of the proposed grid-connected microgrid is chosen based on the higher feasibility of the proposed utility grid-connected microgrid. A detailed analysis of implemented system cases is presented for two different tariff options for the utility grid, including demand response events. The first is the base case system or current system of NITD, which includes only the diesel generator connected with the DVC-NITD-grid, and the second system is the proposed microgrid consisting of solar PV, battery storage and a diesel generator connected to the DVC-NITD-grid (i.e., the main utility grid). The impact of both tariffs and demand response is analyzed for these systems.

7.5.1 Base Case System (Current Existing System of NITD): Diesel Generator Connected to DVC-NITD-Grid The base system or current system is the existing grid-connected (DVC-NITD-grid) system of NITD with a diesel generator. The average electric energy needs the of current existing system at NITD, WB, India, are 11,687.2 kWh/day, 355.5 MWh/ month and 4,265.8 MWh/year, with a peak hourly demand of 1,488.52 kW. The load profile of the base case system for the day on which the largest demand occurs and the energy consumption patterns are shown in Figures 7.6 and 7.7, respectively. It is currently connected to the DVC-NITD-grid with a tariff-1 scheme and diesel generator units of 1,500 kW total capacity. The two optional tariff schemes of the grid utility, tariff scheme-1 and tariff scheme-2 for the case study, are shown in Figure 7.8. NITD is currently spending ₹ 23.99M on the utility bill per year with tariff-1, in which 24% of the utility bill is demand charges, as given in Table 7.4. The total electric consumption of 11,687.2 kWh/day and peak of 1,488.52 kW electrical load in the base case system is served by two energy supply sources, including outage conditions without DR, which is shown in Table 7.3. The annual energy supply is 42,83,279 kWh/yr to fulfill the load demand of 42,64,777 kWh/yr for the base case system without DR. The monthly energy consumption served by sources including both tariffs under DR events for base system is represented in Figure 7.9. The total annual load is reduced to 42,60,657 kWh/yr with DR events; therefore, the energy purchased by the grid utility also decreases for the base case system. The monthly utility charge detail with tariff-1 and tariff-2 for the base case system without and with DR events are given in Tables 7.4 and 7.5, respectively, which include total energy purchased from the grid, energy sold to the grid, net energy purchased, energy charges and demand charges of the utility grid.

Utility Tariff Variation and Demand Response Events

151

FIGURE 7.6  Load profile for the day on which peak demand occurs.

FIGURE 7.7  Energy consumption pattern.

FIGURE 7.8  Two option tariffs for the grid utility, tariff scheme-1 and tariff scheme-2 for the case study.

152

Microgrids

TABLE 7.3 Annual Base Case System Energy Supply and Consumption for Both Tariffs with DR Events Without DR Events

With DR Events

Annual Energy Supply with Both Tariffs

Annual Energy Annual Energy Supply with Reduced Annual Consumption Both Tariffs for DR Events Energy with Both Tariffs Consumption with Both Tariffs for DR Events

Energy source

Total 42,64,777 Energy annual source load kWh/yr

DG set

kWh/yr

31,125

%

0.727 Excess energy kWh/yr

Grid 42,52,154 99.3 Purchases Total

42,83,279 100

Total

18,502 DG set

kWh/yr

24,750

%

Total annual load kWh/yr

42,60,657

0.579 Excess energy kWh/yr

15,414

42,83,279 Grid 42,51,321 99.4 Purchases Total

Total

42,76,070

42,76,071 100

The total charge without DR events for tariff-1 is ₹ 2,39,99,581.82 and for tariff-2 is ₹ 2,42,61,542.36, which are reduced by implementing DR events to ₹ 2,38,08,287.35 for tariff-1 and ₹ 2,40,71,345.90 for tariff-2. The net present and annual cost of the base case system without DR events are ₹ 31,78,10,221.04 and ₹ 2,45,84,011.93 for tariff-1 and ₹ 32,11,96,720.18 and ₹ 2,48,45,972.46 for tariff-2, as specified in Tables 7.6 and 7.7. Also, the net present and annual cost of base case system with DR events are ₹ 31,50,49,871.62 and ₹ 2,43,70,486.82 for tariff-1 and ₹ 31,84,50,565.45 and ₹ 2,46,33,545.37 for tariff-2. The comparative summary for COE, net present cost, operating cost and savings of the base system with respect to DR events, including different tariffs, are shown in Table 7.8, which clearly indicates that the minimum COE, net present cost and operating cost are obtained for tariff-1 under DR events. The relative comparison of COE, operating and net present cost of the base system for both tariffs with DR events are represented in Figure 7.10. The DG operational parameters for both tariffs with and without DR events in the base system are given in Table 7.9. The DG operation and power output profile for both tariffs and DR events under outage for reliability (short outage) and resilience (long outage) for the base system are demonstrated in Table 7.10 and Figure 7.11 separately, which show they operate to maintain the reliability and resilience operation of the system under short and long outage conditions. The long outages correspond to whole-day or multipleday utility outages for natural disasters such as hurricanes or wildfires. It provides an additional value stream by proving the value of hybrid power generation

3,56,052

3,18,927

3,76,548

3,54,091

3,57,403

3,54,219

3,59,643

3,77,332

3,34,566

3,59,390

3,47,543

3,64,131

42,59,844

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Total Annual

Energy Purchased for Both Tariffs (kWh)

Jan

Month

0

0

0

0

0

0

0

0

0

0

0

0

0

Energy Sold for Both Tariffs (kWh)

42,59,844

3,64,131

3,47,543

3,59,390

3,34,566

3,77,332

3,59,643

3,54,219

3,57,403

3,54,091

3,76,548

3,18,927

3,56,052

Net Energy Purchased for Both Tariffs (kWh)

₹ 57,23,737.24

₹ 4,71,814.46

₹ 4,22,547.23

₹ 4,62,379.75

₹ 4,87,801.09

₹ 5,29,250.92

₹ 5,01,718.25

₹ 4,52,765.52

₹ 4,38,159.12

₹ 4,79,816.37

₹ 5,06,099.99

₹ 5,10,848.02

₹ 4,60,536.53

Demand Charge for Both Tariffs

₹ 1,82,75,844.59

₹ 15,54,841.30

₹ 14,84,007.01

₹ 15,41,784.26

₹ 14,35,287.49

₹ 16,18,756.09

₹ 15,42,866.85

₹ 15,26,684.76

₹ 15,40,406.36

₹ 15,26,130.14

₹ 16,22,921.95

₹ 13,61,818.45

₹ 15,20,339.92

Energy Charge for tariff-1

TABLE 7.4  Utility Charges with tariff-1 and tariff-2 for Base Case System without DR Events

₹ 1,85,37,805.12

₹ 15,74,935.07

₹ 15,07,303.75

₹ 15,59,780.98

₹ 14,56,259.98

₹ 16,40,986.85

₹ 15,64,449.51

₹ 15,48,102.06

₹ 15,64,459.69

₹ 15,48,801.67

₹ 16,46,111.79

₹ 13,82,464.96

₹ 15,44,148.80

Energy Charge for tariff-2

₹ 2,39,99,581.82

₹ 20,26,655.76

₹ 19,06,554.24

₹ 20,04,164.01

₹ 19,23,088.58

₹ 21,48,007.00

₹ 20,44,585.10

₹ 19,79,450.28

₹ 19,78,565.48

₹ 20,05,946.51

₹ 21,29,021.94

₹ 18,72,666.47

₹ 19,80,876.45

Total for tariff-1

₹ 2,42,61,542.36

₹ 20,46,749.52

₹ 19,29,850.98

₹ 20,22,160.73

₹ 19,44,061.07

₹ 21,70,237.77

₹ 20,66,167.76

₹ 20,00,867.58

₹ 20,02,618.82

₹ 20,28,618.04

₹ 21,52,211.78

₹ 18,93,312.98

₹ 20,04,685.33

Total for tariff-2

Utility Tariff Variation and Demand Response Events 153

0

42,51,321

3,44,877

3,62,307

Nov

Dec

Total Annual

3,34,707

3,58,765

Sep

Oct

3,60,794

3,77,017

3,55,007

Jun

Jul

3,57,403

May

Aug

0

3,50,218

Apr

0

0

0

0

0

0

0

0

0

0

3,17,236

3,74,528

Feb

0

Energy Sold for Both Tariffs (kWh)

3,58,462

Energy Purchased for Both Tariffs (kWh)

Mar

Jan

Month

42,51,321

3,62,307

3,44,877

3,58,765

3,34,707

3,77,017

3,60,794

3,55,007

3,57,403

3,50,218

3,74,528

3,17,236

3,58,462

Net Energy Purchased for Both Tariffs (kWh)

₹ 57,23,737.24

₹ 4,71,814.46

₹ 4,22,547.23

₹ 4,62,379.75

₹ 4,87,801.09

₹ 5,29,250.92

₹ 5,01,718.25

₹ 4,52,765.52

₹ 4,38,159.12

₹ 4,79,816.37

₹ 5,06,099.99

₹ 5,10,848.02

₹ 4,60,536.53

Demand Charge for Both Tariffs

₹ 1,82,39,250.93

₹ 15,47,051.30

₹ 14,72,622.81

₹ 15,39,101.18

₹ 14,35,894.65

₹ 16,17,404.14

₹ 15,47,807.62

₹ 15,30,078.46

₹ 15,40,405.09

₹ 15,09,439.70

₹ 16,14,215.10

₹ 13,54,597.99

₹ 15,30,632.88

Energy Charge for tariff-1

TABLE 7.5  Utility Charges with Different Tariffs for Base Case System with DR Events

₹ 1,85,02,309.49

₹ 15,67,291.01

₹ 14,96,132.83

₹ 15,57,629.06

₹ 14,56,855.82

₹ 16,40,411.67

₹ 15,70,327.10

₹ 15,49,650.68

₹ 15,64,458.64

₹ 15,32,199.37

₹ 16,36,899.53

₹ 13,75,486.52

₹ 15,54,967.24

Energy Charge for tariff-2

₹ 2,38,08,287.35

₹ 19,96,536.55

₹ 18,70,746.95

₹ 19,78,890.59

₹ 19,23,695.74

₹ 21,17,775.09

₹ 20,49,525.86

₹ 19,82,843.98

₹ 19,78,564.22

₹ 19,60,243.31

₹ 20,92,849.62

₹ 18,65,446.02

₹ 19,91,169.42

Total for tariff-1

₹ 2,40,71,345.90

₹ 20,16,776.26

₹ 18,94,256.97

₹ 19,97,418.47

₹ 19,44,656.91

₹ 21,40,782.62

₹ 20,72,045.34

₹ 20,02,416.21

₹ 20,02,617.76

₹ 19,83,002.99

₹ 21,15,534.05

₹ 18,86,334.55

₹ 20,15,503.77

Total for tariff-2

154 Microgrids

155

Utility Tariff Variation and Demand Response Events

TABLE 7.6 Net Present and Annual Cost with tariff-1 and tariff-2 for Base System without DR Events Cost Components

DG set for Both Tariffs

Utility Charges-1

Utility Charges-2

Total System with tariff-1

Total System with tariff-2

Net Present Cost of System Capital Replacement O&M Fuel Salvage Total

₹ 11,25,000.00 ₹ 0.00

Replacement O&M Fuel Salvage Total

₹ 0.00

₹ 11,25,000.00

₹ 11,25,000.00 ₹ 31,36,54,870.31

₹ 0.00

₹ 0.00

₹ 13,379.98

₹ 31,02,54,991.19

₹ 31,36,41,490.33

₹ 31,02,68,371.17

₹ -1,59,006.52

₹ 0.00

₹ 0.00

₹ -1,59,006.52

₹ 65,75,856.39 ₹ 75,55,229.85

Annual Cost of System Capital

₹ 0.00

₹ 0.00

₹ 87,023.68 ₹ 0.00

₹ 0.00

₹ 31,02,54,991.19

₹ 0.00

₹ 65,75,856.39

₹ 0.00

₹ 65,75,856.39 ₹ -1,59,006.52

₹ 31,36,41,490.33

₹ 31,78,10,221.04

₹ 32,11,96,720.18

₹ 0.00

₹ 87,023.68

₹ 87,023.68

₹ 0.00

₹ 0.00

₹ 0.00

₹ 0.00

₹ 0.00

₹ 1,035.00

₹ 2,39,99,581.82

₹ 2,42,61,542.36

₹ 2,40,00,616.82

₹ 2,42,62,577.36

₹ -12,299.85

₹ 0.00

₹ 0.00

₹ -12,299.85

₹ -12,299.85

₹ 5,08,671.28 ₹ 5,84,430.10

₹ 0.00

₹ 2,39,99,581.82

₹ 0.00

₹ 2,42,61,542.36

₹ 5,08,671.28

₹ 2,45,84,011.93

₹ 5,08,671.28

₹ 2,48,45,972.46

TABLE 7.7 Net Present and Annual Cost with Different Tariffs for Base System with DR Events Cost Components DG set for Both Utility Charges-1 Utility Charges-2 Total System with Total System with Tariffs tariff-1 tariff-2 Net Present Cost of System Capital Replacement O&M Fuel Salvage Total

₹ 11,25,000.00

₹ 0.00

₹ 12,798.24

₹ 62,89,949.59

₹ -1,59,904.86

₹ 72,67,842.97

Annual Cost of System Capital Replacement O&M Fuel Salvage Total

₹ 0.00

₹ 11,25,000.00

₹ 11,25,000.00

₹ 30,77,82,028.65 ₹ 31,11,82,722.48 ₹ 30,77,94,826.89

₹ 31,11,95,520.72

₹ 0.00

₹ 0.00 ₹ 0.00

₹ 0.00

₹ 0.00

₹ 0.00 ₹ 0.00

₹ 0.00

₹ 62,89,949.59 ₹ -1,59,904.86

₹ 30,77,82,028.65 ₹ 31,11,82,722.48 ₹ 31,50,49,871.62

₹ 0.00

₹ 62,89,949.59

₹ -1,59,904.86

₹ 31,84,50,565.45

₹ 87,023.68

₹ 0.00

₹ 0.00

₹ 87,023.68

₹ 87,023.68

₹ 990.00

₹ 2,38,08,287.35

₹ 2,40,71,345.90

₹ 2,38,09,277.35

₹ 2,40,72,335.90

₹ 0.00

₹ 4,86,555.14 ₹ -12,369.34

₹ 5,62,199.47

₹ 0.00

₹ 0.00 ₹ 0.00

₹ 2,38,08,287.35

₹ 0.00

₹ 0.00 ₹ 0.00

₹ 2,40,71,345.90

₹ 0.00

₹ 4,86,555.14

₹ -12,369.34

₹ 2,43,70,486.82

₹ 0.00

₹ 4,86,555.14

₹ -12,369.34

₹ 2,46,33,545.37

156

Microgrids

TABLE 7.8 Comparative Summary of Base System for Both Tariffs with DR Events Parameters

Without DR Events Tariff-1

COE (₹/kWh) Net present cost (₹) Operating cost (₹)

Tariff-2

5.76 ₹/kWh

₹ 317810200

₹ 24496990

Utility bill savings (₹)

NA (Base case)

Total bill savings (₹)

NA (Base case)

5.82 ₹/kWh

₹ 321196700

₹ 24758950 ₹ -261962

₹ -3386518

With DR Events Tariff-1 5.72 ₹/kWh

₹ 315049900 ₹ 24283460

Tariff-2 5.78 ₹/kWh

₹ 318450600

NA (Base case) NA (Base case)

₹ 24546520

₹ -263060

₹ -3400713

TABLE 7.9 DG Set Operational Parameters for Both Tariffs with DR Event Effects in Base System Parameters

Value for Both Tariffs without DR Events

Value for Both Tariffs with DR Events

Hours of Operation

83.0 hrs/yr

66.0 hrs/yr

Number of Starts

11.0 starts/yr

11.0 starts/yr

Operational Life

181 yr

227 yr

Electrical Production

31,125 kWh/yr

24,750 kWh/yr

Mean Electrical Output

375 kW

375 kW

Fuel Consumption

9,271 L

7,372 L/yr

Generator Fuel Price

66.0 ₹/L

66.0 ₹/L

TABLE 7.10 DG Operation for Both Tariffs and DR Events under Outage for Reliability (Short Outage) and Resilience (Long Outage) for Base System Components DG runtime (hours/day) DG O&M cost (₹/day)

DG fuel consumption (L/day) DG fuel cost (₹/day)

Value 24.0 360 2,681 1,76,929

during outages, which are considered for resilience analysis. The shorter outages are more frequent outages defined for the reliability analysis. In this case study, along outage is defined and simulated that occurs once in every 1.0  years. It is assumed that the grid shutdown starts on 21 September  03:00 and ends on 23

157

Utility Tariff Variation and Demand Response Events

TABLE 7.11 Pollutant Emissions of the Base Case System for Both Tariffs with DR Events Pollutant Emission

Emission Amount for Both Tariffs without DR Events

Emission Amount for Both Tariffs with DR Events

Carbon Dioxide

27,11,629 kg/yr

27,06,132 kg/yr

153 kg/yr

122 kg/yr

Carbon Monoxide Unburned Hydrocarbons Particulate Matter Sulfur Dioxide Nitrogen Oxides

6.68 kg/yr

5.31 kg/yr

0.927 kg/yr

0.737 kg/yr

11,710 kg/yr

11,696 kg/yr

5,842 kg/yr

5,811 kg/yr

FIGURE 7.9  Monthly energy consumption served by sources including both tariffs for base system (a) without DR events, (b) with DR events.

September 03:00. Also, short outages occur 10 times per year on average with a 2.0 hour average repair time. The system performance during these outages are shown in Figure 7.11(b) and (c). The pollutant emissions for the base case system considering the different tariffs under DR events are reported in Table 7.11. The emissions of these pollutants are considered from the production of electricity by generators and consumption of grid electricity. The pollutant emissions of the base system such as carbon dioxide without DR and with DR events are 27,11,629 kg/ yr and 27,06,132 kg/yr, and the particulate matter without DR and with DR events are 0.927 kg/yr and 0.737 kg/yr.

158

Microgrids

FIGURE 7.10  COE, operating and net present cost of base system for both tariffs with DR events.

FIGURE 7.11  (a) Power output pattern of DG under outage, (b) grid outage and reliability (short outage) and resilience (long outage), (c) system operation under (long outage under resilience) for base system including DR events for both tariffs.

159

Utility Tariff Variation and Demand Response Events

7.5.2 Proposed Microgrid System: Solar PV-Battery StorageDiesel Generator Connected to Main Utility Grid The proposed microgrid system includes the installation of such components as solar PV, wind battery storage, a system converter and diesel generator connected with the DVC-NITD-grid system, which is obtained as a feasible solution based on the minimum values of the cost of energy and net present cost. This microgrid system proposes adding 500 kW of solar PV, 4,902 kWh/5,515 kWh of battery capacity and 1,042 kW/977 kW system converter with the base case system for different tariffs, as given in Table 7.12. The proposed microgrid system reduces the system annual utility bill to ₹ 18.1M for tariff-1 and ₹ 17.68M for tariff-2 without DR events; with DR events, the bills are ₹ 17.83M for tariff-1 and ₹ 17.44M tariff-2. Solar PV: The solar PV system’s detailed operational parameters for the proposed microgrid are given in Table 7.13, which specifies that the solar PV has a nominal

TABLE 7.12 Component Installation Detail for the Proposed Microgrid for Different Tariffs, Including DR Events Obtained by HOMER-grid Components

Tariff-1 Size

Solar PV

500 kW

Battery Storage

4,902 kWh

System Converter

1,042 kW

DG Set

1,500 kW

Tariff-2

Installation/ Capital Cost ₹ 10,69,394

₹ 26,96,100

Size

Total Installation/ Capital Cost

500 kW 5,515 kWh

3,12,600

977 kW

₹ 11,25,000.00

1,500 kW

₹ 10,69,394

₹ 30,33,250

₹ 2,93,062.5

₹ 11,25,000.00

TABLE 7.13 Solar PV Details for Both Tariffs and DR Events Parameters

Values

Rated capacity

500 kW

Mean output

87.1 kW

Mean output per day

2,090 kWh/d

Capacity factor

17.4%

Total production

7,62,863 kWh/yr

Minimum output

0 kW

Maximum output

498 kW

PV penetration

17.8%

Hours of operation

4,390 hrs/yr

PV levelized cost

0.11 ₹/kWh

160

Microgrids

capacity of 500 kW with the annual production of 7,62,863 kWh/yr. The yearly operating hours are 4,390 hrs/yr, with a per-day mean output of 2,090 kWh/day. Battery Storage System: The battery storage system has a nominal capacity of 4,902 kWh/5,515 kWh and specific annual throughput considering different tariffs with DR events. The detailed operational parameters of battery storage for the proposed microgrid system are specified in Table 7.14. Diesel Generator Set: The diesel generator set has a capacity of 1,500 kW which provides different annual power outputs during operation, including different operation hours for different tariffs with and without DR events. The operational parameter summary of DG for the proposed microgrid system is given in Table 7.15. System Converter: The system converter has two operating modes, inverter mode and rectifier mode, with a nominal capacity of 1,042 kW/ 977 kW for different tariffs with and without DR events. The detailed operational parameters of the system converter for the proposed microgrid system are given in Table 7.16. The total electric energy supplied by distinct sources like the solar PV, DG set and utility grid considered without DR events are 44,34,765 kWh/yr for tariff-1 and 45,12,505 kWh/yr for tariff-2, and with DR events, they are 44,24,594 kWh/yr for tariff-1 and 45,06,388 kWh/yr for tariff-2, as illustrated in Table 7.17. The monthly energy consumption served by energy sources solar PV, DG and grid for the proposed microgrid considered without and with DR events is represented in Figure 7.12. The supplied energy is mainly utilized to fulfill the load demand and to charge the batteries; also, a portion is used to support system losses. The total monthly grid utility charges for the proposed grid-connected solar PV-based microgrid system without DR events are ₹ 1,80,90,790.08 for tariff-1 and ₹ 1,76,80,120.03 for tariff-2 per year, as illustrated in Tables  7.18 and 7.19, respectively. Similarly, the total grid utility charges for the proposed microgrid system with DR events are ₹ 1,78,32,177.29 for tariff-1 and ₹ 1,74,42,214.90 for tariff-2 annually, as shown in Tables 7.20 and 7.21 individually.

TABLE 7.14 Battery Storage Operational Details for Both Tariffs and DR Events Parameters

Unit

Without DR Tariff-1

With DR

Tariff-2

Tariff-1

Tariff-2

Rated capacity

kWh

4,902

5,515

4,902

5,515

Maintenance cost

₹/yr

49,020

55,150

49,020

55,150

8

9

8

8

Autonomy

hr

Annual throughput

kWh/yr

6,39,811

9,98,297

6,36,683

10,09,627

Energy in

kWh/yr

6,74,420

10,51,775

6,71,123

10,63,671

Energy out

kWh/yr

6,06,978

9,47,068

6,04,010

9,57,816

Battery wear cost

₹/kWh

0.193

0.193

0.193

0.193

Losses

kWh/yr

62,540

99,688

62,210

1,01,493

Storage depletion

kWh/yr

−4,902

−5,020

−4,902

−4,362

161

Utility Tariff Variation and Demand Response Events

TABLE 7.15 Diesel Generator Operational Details Parameters

Unit

Without DR Tariff-1

With DR

Tariff-2

Tariff-1

Tariff-2

Hours of operation

hrs/yr

5

8

5

6

Number of starts

starts/yr

1

3

1

2

Operational life

yr

3,000

1,875

3,000

2,500

Capacity factor

%

Fixed generation cost

₹/hr

1,597

kWh/yr

4,126

5,883

4,126

4,995

Marginal generation cost Electrical production

0.0314

0.0448 1,597

15.6

₹/kWh

0.0314

0.038

1,597

15.6

1,597

15.6

15.6

Mean electrical output

kW

825

735

825

833

Minimum electrical output

kW

404

375

404

392

Maximum electrical output

kW

1,369

1,333

1,369

1,141

Fuel consumption

L/yr

1,090

1,574

1,090

1,318

Specific fuel consumption

L/kWh

Fuel energy input

kWh/yr

Mean electrical efficiency

%

0.264

0.268

10,723

0.264

15,487

38.5

0.264

10,723

38

12,969

38.5

38.5

TABLE 7.16 System Converter Operational Details Parameters

Unit

Without DR Tariff-1

Tariff-2

Inverter Rectifier Mode Mode Operational capacity

kW

1,042

Mean output

With DR

1,042

Tariff-1

Tariff-2

Rectifier Inverter Rectifier Mode Mode Mode

Inverter Mode

Rectifier Mode

977

977

1,042

1,042

977

977 119

kW

141

182

116

141

69

184

Minimum output kW

0

0

0

0

0

0

0

0

Maximum output kW

963

1,042

977

977

963

1,042

977

977

14

69.4

Inverter Mode

Capacity factor

%

Hours of operation

hrs/yr

Energy out

kWh/yr12,37,8236,07,585

15,94,670 10,20,444 12,35,0746,04,340

16,14,834 10,42,817

Energy in

kWh/yr13,02,9716,39,563

16,78,600 10,74,152 13,00,0786,36,147

16,99,825 10,97,702

Losses

kWh/yr

4,866

6.66 2,287

65,149 31,978

18.6 5,062

83,930

11.9 2,932

53,708

14 4,871

6.62 2,274

65,004 31,807

18.9 5,212

84,991

12.2 2,769

54,885

162

Microgrids

TABLE 7.17 Annual Energy Supply Summary of Proposed Microgrid for Different Tariffs and DR Events Without DR Tariff-1 Energy Source

kWh/yr

Solar PV

7,62,863

DG Set

4,126

Grid 36,67,775 Purchases Total

With DR Tariff-2

% 17.2 0.0930 82.7

44,34,765 100

Tariff-1

kWh/yr

%

kWh/yr

7,62,863

16.9

7,62,863

5,883 37,43,759

0.130 83.0

45,12,505 100

4,126 36,57,604

Tariff-2 %

17.2 0.0933 82.7

44,24,594 100

kWh/yr

%

7,62,863

16.19

4,995 37,38,529

0.111 83.0

45,06,388 100

FIGURE 7.12  Monthly energy consumption served by sources for proposed microgrid: (a) without DR events, (b) with DR events.

The total utility charges comprise the total energy purchased from and sold to the grid, net energy purchased, energy charges and demand charges of the utility grid. The net present and annual cost for the proposed microgrid system without DR events are ₹ 24,15,05,393.47 and ₹ 1,86,81,499.46 for tariff-1 and ₹ 23,71,30,140.96 and ₹ 1,83,43,054.53 for tariff-2, respectively, as listed in Tables 7.22 and 7.23. In the same way, the net present and annual cost for the proposed microgrid system with DR events are ₹ 23,81,62,172.41 and ₹ 1,84,22,886.68 for tariff-1 and ₹ 23,33,92,753.81 and ₹1,80,53,951.27 for tariff-2, as enumerated in Tables  7.24 and 7.25. The total net present and annual cost of the system consist of capital cost, replacement cost, O&M cost, fuel cost and salvage cost.

163

Utility Tariff Variation and Demand Response Events

TABLE 7.18 Utility Charges with tariff-1 for Proposed Microgrid without DR Events Month

Energy Energy Sold Net Energy Energy Charge Purchased (kWh) Purchased (kWh) (kWh)

Jan

3,00,693

0

3,00,693

Feb

2,63,992

0

2,63,992

Mar

3,14,426

0

3,14,426

Apr

2,95,886

0

2,95,886

May

3,03,882

1.16

3,03,881

Jun

3,08,966

0

3,08,966

Jul

3,24,228

0

3,24,228

Aug

3,42,148

0

3,42,148

Sep

2,99,526

0

2,99,526

Oct

3,12,638

0

3,12,638

Nov

2,97,317

0

2,97,317

Dec

3,04,073

0

3,04,073

Total Annual 36,67,775

1.16

36,67,774

Demand Charge

Total Charges

₹ 12,83,960.59

₹ 1,87,332.83

₹ 14,71,293.43

₹ 13,55,174.17

₹ 1,74,334.31

₹ 15,29,508.48

₹ 11,27,246.43

₹ 12,75,266.80

₹ 13,09,727.73

₹ 13,31,644.07 ₹ 13,90,936.39

₹ 14,67,815.89 ₹ 12,84,966.15

₹ 1,68,287.17

₹ 1,92,426.35

₹ 1,71,954.63

₹ 2,46,467.60 ₹ 1,96,898.29

₹ 2,06,187.33

₹ 2,15,191.43

₹ 13,41,216.74

₹ 1,73,512.81

₹ 12,98,392.49

₹ 2,26,215.99

₹ 12,69,545.56

₹ 1,96,088.32

₹ 12,95,533.61 ₹ 14,67,693.14

₹ 14,81,682.36 ₹ 15,78,111.67

₹ 15,87,834.67 ₹ 16,74,003.21

₹ 15,00,157.58

₹ 15,14,729.55

₹ 14,65,633.88 ₹ 15,24,608.49

₹ 1,57,35,893.00 ₹ 23,54,897.07 ₹ 1,80,90,790.08

TABLE 7.19 Utility Charges with tariff-2 for Proposed Microgrid without DR Events Month

Jan

Energy Energy Sold Net Energy Energy Charge Purchased (kWh) Purchased (kWh) (kWh) 3,04,590

0

3,04,590

Feb

2,67,808

0

2,67,808

Mar

3,17,445

0

3,17,445

Apr

3,01,985

0

3,01,985

May

3,07,151

0

3,07,151

Jun

3,23,845

0

3,23,845

Jul

3,29,718

0

3,29,718

Aug

3,47,811

0

3,47,811

Sep

3,07,123

0

3,07,123

Oct

3,14,869

0

3,14,869

Nov

3,03,859

0

3,03,859

Dec

3,17,556

0

3,17,556

Total Annual 37,43,759

0

37,43,759

₹ 12,58,583.36

Demand Charge ₹ 1,74,140.38

₹ 14,32,723.73

₹ 1,71,072.34

₹ 15,02,990.68

₹ 11,09,061.71

₹ 1,63,531.23

₹ 12,54,820.07

₹ 1,78,681.62

₹ 13,31,918.34

₹ 12,84,864.76 ₹ 13,00,172.92

₹ 13,60,999.93

₹ 14,35,909.59 ₹ 12,60,182.69 ₹ 13,17,800.75 ₹ 12,47,904.23

₹ 12,72,888.94

Total Charges

₹ 1,70,699.49 ₹ 2,33,376.15

₹ 1,91,508.72

₹ 2,00,123.00 ₹ 2,01,348.96 ₹ 1,64,634.35 ₹ 1,82,773.68

₹ 2,13,122.83

₹ 12,72,592.94

₹ 14,33,501.69 ₹ 14,55,564.25

₹ 15,33,549.07

₹ 15,52,508.65

₹ 16,36,032.59 ₹ 14,61,531.65

₹ 14,82,435.10 ₹ 14,30,677.91

₹ 14,86,011.77

₹ 1,54,35,107.28 ₹ 22,45,012.75 ₹ 1,76,80,120.03

164

Microgrids

TABLE 7.20 Utility Charges with tariff-1 for Proposed Microgrid with DR Events Month

Energy Energy Sold Net Energy Energy Charge Purchased (kWh) Purchased (kWh) (kWh)

Jan

3,02,397

0

3,02,397

Feb

2,62,808

0

2,62,808

Mar

3,12,132

0

3,12,132

Apr

2,92,617

0

2,92,617

May

3,03,882

1.16

3,03,881

Jun

3,09,581

0

3,09,581

Jul

3,24,954

0

3,24,954

Aug

3,41,179

0

3,41,179

Sep

2,99,595

0

2,99,595

Oct

3,11,472

0

3,11,472

Nov

2,94,798

0

2,94,798

Dec

3,02,189

0

3,02,189

Total Annual 36,57,604

1.16

36,57,603

Demand Charge

Total Charges

₹ 12,91,234.87

₹ 1,87,332.83

₹ 14,78,567.71

₹ 13,45,289.15

₹ 1,75,421.64

₹ 14,82,185.57

₹ 11,22,190.48

₹ 12,61,178.95

₹ 13,09,726.93 ₹ 13,34,294.91

₹ 13,94,052.05

₹ 1,68,287.17

₹ 1,91,051.89

₹ 14,81,681.57

₹ 1,96,898.29

₹ 15,90,950.33

₹ 2,15,191.43

₹ 15,00,455.70

₹ 2,46,467.60

₹ 2,06,719.03

₹ 13,36,213.21

₹ 1,73,284.41

₹ 12,58,787.42

₹ 12,90,348.68

₹ 14,10,916.83

₹ 1,71,954.63

₹ 14,63,656.28 ₹ 12,85,264.27

₹ 12,90,477.66

₹ 1,95,782.25

₹ 2,27,060.70

₹ 15,80,762.51

₹ 16,29,180.07 ₹ 14,81,790.26

₹ 14,19,700.51

₹ 14,85,508.57

₹ 1,56,92,237.21 ₹ 23,55,451.87 ₹ 1,78,32,177.29

TABLE 7.21 Utility Charges with tariff-2 for Proposed Microgrid with DR Events Month

Energy Energy Sold Net Energy Energy Charge Purchased (kWh) Purchased (kWh) (kWh)

Jan

3,07,785

0

3,07,785

Feb

2,66,182

0

2,66,182

Mar

3,15,558

0

3,15,558

Apr

2,98,466

0

2,98,466

May

3,06,794

0

3,06,794

Jun

3,24,050

0

3,24,050

Jul

3,30,875

0

3,30,875

Aug

3,47,279

0

3,47,279

Sep

3,08,543

0

3,08,543

Oct

3,14,614

0

3,14,614

Nov

3,01,813

0

3,01,813

Dec

3,16,569

0

3,16,569

Total Annual 37,38,529

0

37,38,529

Demand Charge

Total Charges

₹ 12,59,174.23 ₹ 1,87,332.83

₹ 14,46,507.06

₹ 13,16,506.13 ₹ 1,76,871.40

₹ 14,54,138.76

₹ 12,82,568.48 ₹ 1,71,954.63

₹ 14,54,523.12

₹ 13,60,849.54 ₹ 1,96,898.29

₹ 15,57,747.82

₹ 10,98,546.97 ₹ 1,68,287.17 ₹ 12,26,294.06 ₹ 1,91,051.89

₹ 12,97,106.33 ₹ 2,46,467.60

₹ 14,26,285.98 ₹ 2,06,719.03

₹ 12,51,752.70 ₹ 2,15,191.43

₹ 13,03,614.44 ₹ 1,77,486.39

₹ 12,25,526.23 ₹ 1,95,782.25 ₹ 12,53,678.06 ₹ 2,27,060.70

₹ 12,66,834.14

₹ 13,76,031.94

₹ 15,43,573.93

₹ 15,91,809.77

₹ 14,66,944.13

₹ 14,48,826.97

₹ 13,86,439.31 ₹ 14,48,837.95

₹ 1,53,01,903.14 ₹ 23,61,103.61 ₹ 1,74,42,214.90

165

Utility Tariff Variation and Demand Response Events

TABLE 7.22 Net Present and Annual Cost with tariff-1 for Proposed Microgrid without DR Events Cost Components

System

Generic Flat Plate PV

Diesel Generator

Grid Tariff-1

Battery Storage

System Converter

Net Present Cost of System Capital Replacement O&M Fuel Salvage Total

₹ 52,03,093.94

₹ 12,76,512.67

₹ 23,45,14,437.42

₹ 9,29,773.15

₹ -4,18,423.72

₹ 10,69,393.94 ₹ 11,25,000.00 ₹ 0.00

₹ 10,772.93 ₹ 0.00

₹ 0.00

₹ 0.00 ₹ 26,96,100.00

₹ 0.00 ₹ 11,43,884.67

₹ 969.56 ₹ 23,38,68,988.07

₹ 9,29,773.15

₹ 0.00 ₹ -1,78,171.15

₹ 0.00

₹ 6,33,706.86 ₹ 0.00

₹ 0.00 ₹ -2,15,290.64

₹ 24,15,05,393.47 ₹ 10,80,166.87 ₹ 18,77,571.56 ₹ 23,38,68,988.07 ₹ 42,58,400.89

Annual Cost of System Capital Replacement O&M Fuel Salvage Total

₹ 4,02,482.09 ₹ 98,743.84

₹ 1,81,40,718.41

₹ 71,922.02

₹ -32,366.91

₹ 1,86,81,499.46

₹ 82,722.30 ₹ 0.00

₹ 833.33 ₹ 0.00

₹ 0.00

₹ 83,555.64

₹ 87,023.68

₹ 0.00

₹ 2,08,555.14

₹ 75.00

₹ 1,80,90,790.08

₹ 49,020.00

₹ -13,782.32

₹ 0.00

₹ -16,653.67

₹ 0.00

₹ 71,922.02

₹ 1,45,238.38

₹ 0.00 ₹ 0.00

₹ 1,80,90,790.08

₹ 88,484.49 ₹ 0.00

₹ 3,29,405.95

₹ 3,12,600.00

₹ 1,32,628.00 ₹ 0.00

₹ 0.00

₹ -24,961.93

₹ 4,20,266.07 ₹ 24,180.98 ₹ 10,259.36 ₹ 0.00

₹ 0.00

₹ -1,930.91

₹ 32,509.42

TABLE 7.23 Net Present and Annual Cost with tariff-2 for Proposed Microgrid without DR Events Cost Components

System

Generic Flat Plate PV

Diesel Generator

Grid Tariff-2

Battery Storage

System Converter

Net Present Cost of System Capital Replacement O&M Fuel Salvage Total

₹ 55,20,706.44 ₹ 10,69,393.94 ₹ 11,25,000.00

₹ 14,17,391.73

₹ 22,92,85,321.06

₹ 13,42,865.81

₹ -4,36,144.08

₹ 0.00

₹ 10,772.93

₹ 0.00

₹ 0.00 ₹ 30,33,250.00 ₹ 0.00 ₹ 12,93,052.98

₹ 1,551.30 ₹ 22,85,60,044.29

₹ 0.00 ₹ 13,42,865.81

₹ 0.00 ₹ -1,77,272.81

₹ 0.00

₹ 0.00

₹ ₹ 10,80,166.87 ₹ 22,92,144.30 ₹ 23,71,30,140.96 22,85,60,044.29

Replacement O&M Fuel Salvage Total

₹ 4,27,050.81

₹ 82,722.30

₹ 1,77,36,223.36

₹ 833.33

₹ -33,737.65

₹ 0.00

₹ 1,09,641.46 ₹ 1,03,876.55

₹ 1,83,43,054.53

₹ 0.00

₹ 0.00

₹ 83,555.64

₹ 0.00

₹ -2,35,469.46

₹ 1,24,338.75 ₹ 0.00 ₹ 0.00

₹ -23,401.81

₹ 48,03,786.06

₹ 3,93,999.44

₹ 0.00

₹ 2,34,635.17

₹ 22,669.67

₹ 120.00 ₹ 1,76,80,120.03

₹ 55,150.00

₹ 0.00

Annual Cost of System Capital

₹ 7,12,952.54

₹ 2,93,062.50

₹ 87,023.68 ₹ 0.00

₹ 1,03,876.55 ₹ -13,712.83

₹ 0.00

₹ 0.00 ₹ 0.00

₹ 1,77,307.40 ₹ 1,76,80,120.03

₹ 1,00,023.31 ₹ 0.00

₹ -18,214.59

₹ 3,71,593.88

₹ 9,618.15 ₹ 0.00

₹ -1,810.23

₹ 30,477.58

166

Microgrids

TABLE 7.24 Net Present and Annual Cost with tariff-1 for Proposed Microgrid with DR Events Cost Components

System

Generic Flat Plate PV

Diesel Generator

Grid Tariff-1

Battery Storage

System Converter

Net Present Cost of System Capital Replacement O&M Fuel Salvage Total

₹ 52,03,093.94 ₹ 10,69,393.94 ₹ 11,25,000.00

₹ 12,76,512.67

₹ 23,11,71,216.37

₹ 9,29,773.15

₹ 0.00

₹ 10,772.93 ₹ 0.00

₹ -4,18,423.72

₹ 0.00 ₹ 26,96,100.00

₹ 0.00

₹ 0.00 ₹ 11,43,884.67

₹ 969.56 ₹ 23,05,25,767.02

₹ 9,29,773.15

₹ 0.00

₹ 0.00 ₹ -1,78,171.15

₹ 0.00

₹ ₹ 10,80,166.87 ₹ 18,77,571.56 ₹ 23,81,62,172.41 23,05,25,767.02

Replacement O&M Fuel Salvage Total

₹ 4,02,482.09

₹ 82,722.30

₹ 1,78,82,105.62

₹ 833.33

₹ -32,366.91

₹ 0.00

₹ 98,743.84 ₹ 71,922.02

₹ 1,84,22,886.68

₹ 0.00 ₹ 0.00

₹ 83,555.64

₹ 0.00

₹ -2,15,290.64

₹ 1,32,628.00 ₹ 0.00

₹ 0.00

₹ -24,961.93

₹ 42,58,400.89

₹ 4,20,266.07

₹ 0.00

₹ 2,08,555.14

₹ 24,180.98

₹ 75.00 ₹ 1,78,32,177.29

₹ 49,020.00

₹ 0.00

Annual Cost of System Capital

₹ 6,33,706.86

₹ 3,12,600.00

₹ 87,023.68

₹ 0.00

₹ 0.00

₹ 71,922.02

₹ 0.00

₹ -13,782.32

₹ 0.00

₹ 1,45,238.38 ₹ 1,78,32,177.29

₹ 88,484.49 ₹ 0.00

₹ -16,653.67

₹ 3,29,405.95

₹ 10,259.36 ₹ 0.00

₹ -1,930.91

₹ 32,509.42

TABLE 7.25 Net Present and Annual Cost with tariff-2 for Proposed Microgrid with DR Events Cost Components

System

Generic flat plate PV

Diesel Generator

Grid Tariff-2

Battery Storage

System Converter

Net Present Cost of System Capital Replacement O&M Fuel Salvage Total

₹ 51,83,556.44

₹ 13,16,536.18

₹ 10,69,393.94 ₹ 11,25,000.00 ₹ 0.00 ₹ 0.00

₹ 0.00

₹ 0.00

₹ 11,24,488.79 ₹ 0.00

₹ 22,61,30,165.00 ₹ 10,772.93

₹ 11,24,488.79 ₹ -3,61,992.60

₹ 0.00

₹ 1,163.48

₹ 0.00

₹ 26,96,100.00

₹ 11,92,197.43

₹ 22,54,84,521.73 ₹ 6,33,706.86

₹ -1,77,871.70 ₹ 0.00

₹ 0.00

Replacement O&M Fuel Salvage Total

₹ 1,24,338.75

₹ 0.00 ₹ 0.00

₹ -1,60,719.09

₹ -23,401.81

₹ 23,33,92,753.81 ₹ 10,80,166.87 ₹ 20,72,780.56 ₹ 22,54,84,521.73 ₹ 43,61,285.20

Annual Cost of System Capital

₹ 2,93,062.50

₹ 3,93,999.44

₹ 4,00,970.78

₹ 82,722.30

₹ 87,023.68

₹ 0.00

₹ 2,08,555.14

₹ 22,669.67

₹ 1,74,92,158.23

₹ 833.33

₹ 90.00

₹ 1,74,42,214.90

₹ 49,020.00

₹ 0.00

₹ 1,01,839.84 ₹ 86,984.13

₹ -28,001.71

₹ 1,80,53,951.27

₹ 0.00

₹ 0.00 ₹ 0.00

₹ 83,555.64

₹ 0.00

₹ 86,984.13

₹ -13,759.15

₹ 1,60,338.65

₹ 0.00

₹ 0.00 ₹ 0.00

₹ 1,74,42,214.90

₹ 92,221.69 ₹ 0.00

₹ -12,432.32

₹ 3,37,364.50

₹ 9,618.15 ₹ 0.00

₹ -1,810.23

₹ 30,477.58

167

Utility Tariff Variation and Demand Response Events

A comparative analysis of the proposed microgrid system for COE, net present cost, operating cost and savings with respect to DR events including different tariffs is shown in Table 7.26, which indicates that the minimum COE (4.24 ₹/kWh), net present cost (₹ 23,33,92,754) and operating cost (₹ 22,61,30,165.0) are achieved with tariff-2 under DR events. The relative graphical representations of COE, operating and net present cost of the base system for both tariffs with DR events are represented in Figure 7.13. Resilience is considered for long-duration outages and reliability for short outages. These are the ability of the system of a facility to respond to an extended, multi-day utility outage and short-duration outage, for example, 2 hr. The assumptions related to outage for the presented case study are discussed in the previous section. The annual operational parameters of the DG set under normal and outage conditions for the proposed microgrid together with both tariffs and DR events are presented in Tables  7.27, 7.28 and 7.29 to maintain resilience (long-duration outage) and reliability (short-duration outage) within the proposed grid-connected microgrid. The performance of the proposed microgrid during outages for resilience

TABLE 7.26 Comparative Summary of Proposed Microgrid for Different Tariffs and DR Events Parameters

Without DR Events Tariff-1

COE (₹/kWh) Net present cost (₹)

Operating cost (₹)

Utility Bill savings (₹) Total Bill savings (₹) Payback (yr)

With DR Events

Tariff-2

4.37 ₹/kWh

₹ 24,15,05,393

Tariff-1

4.30 ₹/kWh

₹ 23,71,30,140

Tariff-2

4.32 ₹/kWh

₹ 23,81,62,172

₹ 23,45,14,437.42 ₹ 22,92,85,321.06 ₹ 23,11,71,216.3 ₹ 59,08,791

₹ 7,64,00,000

0.65

₹ 63,19,461

₹ 8,17,00,000

0.66

₹ 59,76,110

₹ 7,73,00,000

0.64

4.24 ₹/kWh

₹ 23,33,92,754

₹ 22,61,30,165.0 ₹ 63,66,072

₹8,23,00,000

0.60

FIGURE 7.13  COE, operating and net present cost of proposed grid-connected microgrid with DR events.

168

Microgrids

TABLE 7.27 DG Set Operational Parameters for Both Tariffs and DR Events for Proposed Microgrid Parameters

Without DR Events Tariff-1

With DR Events

Tariff-2

Tariff-1

Tariff-2

Hours of Operation

5.0 hrs/yr

8.0 hrs/yr

5.0 hrs/yr

6.0 hrs/yr

Number of Starts

1.0 starts/yr

3.0 starts/yr

1.0 starts/yr

2.0 starts/yr

Operational Life

3000 yr

1875 yr

3000 yr

2,500 yr

Electrical Production

4,126 kWh/yr

5883 kWh/yr

4,126 kWh/yr

4995 kWh/yr

Mean Electrical Output

825 kW

735 kW

825 kW

833 kW

Fuel Consumption

1,090 L/yr

1,574 L/yr

1,090 L/yr

1,318L/yr

Generator Fuel Price

66.0 ₹/L

66.0 ₹/L

66.0 ₹/L

66.0 ₹/L

TABLE 7.28 DG Operation for Different Tariffs and DR Events under Outage for Reliability (Short Outage) for Proposed Microgrid Components

Without DR Events Tariff-1

DG runtime (hours/day)

Battery throughput (kWh/day)

Tariff-1

2.78

26.1

DG O&M cost (₹/day) DG fuel cost (₹/day)

Tariff-2

1.74

DG fuel consumption (L/day)

With DR Events

41.7

Tariff-2

1.82

2.18

27.3

32.7

379

547

396

479

25,016

36,131

26,153

31,631

1,675

1,549

1,655

1,790

TABLE 7.29 DG Operation for Different Tariffs and DR Events under Outage for Resilience (Long Outage) for Proposed Microgrid Components

Without DR Events Tariff-1

DG runtime (hours/day) DG O&M cost (₹/day) DG fuel consumption (L/day) DG fuel cost (₹/day)

Battery throughput (kWh/day)

2.50 37.5

Tariff-2 2.00 30.0

With DR Events Tariff-1 2.50 37.5

Tariff-2 2.00 30.0

545

550

545

538

35,961

36,276

35,961

35,539

1,903

1,627

1,903

1,944

169

Utility Tariff Variation and Demand Response Events

(long outage) and reliability (short outage) without and with DR events are illustrated in Figures 7.14 and 7.15. The performance of the proposed grid-connected microgrid system during resilience (long outage) and reliability (short outage) is maintained by combined operation of renewable (solar PV) power, battery power and generator power only, which fulfill the load demand under long- and short-duration outages to maintain the resilience and reliability of the system. The pollutant emissions for the proposed microgrid system without DR events such as carbon dioxide and particulate matter are 23,20,887

1500 State of Charge (%)

1250 kW

1000

Battery

99

82.5

750 500

66

49.5

250 0 21-09 22-09 03:00 12:00 Consumption

22-09 09:00

33

16.5

22-09 23-09 23-09 06:00 03:00 12:00 Renewable Power

0 21-09 03:00

Generator Power

22-09 12:00

22-09 09:00

22-09 06:00

23-09 03:00

23-09 12:00

(a) 1200

120

1000 600 400 200 0 21-09 22-09 22-09 03:00 12:00 09:00 Consumption Generator Power

22-09 23-09 23-09 06:00 03:00 12:00 Renewable Power

State of Charge (%)

kW

Battery

100

800

80 60 40 20 0 21-09 03:00

22-09 12:00

22-09 09:00

22-09 06:00

23-09 03:00

23-09 12:00

(b)

270 225 180 135 90 45 0

26-02 03:00

Consumption

26-02 04:00 Renewable Power

State of Charge (%)

kW

FIGURE 7.14  Proposed microgrid performance during outages for resilience (long outage): (a) without DR events, (b) with DR events.

60 50 40 30 20 10 0

Battery

26-02 03:00

26-02 04:00

Generator Power

FIGURE 7.15  Proposed microgrid performance during outages for reliability (short outage) with DR events.

170

Microgrids

kg/yr and 0.109 kg/yr for tariff-1 and 23,70,176 kg/yr and 0.157 kg/yr for tariff-2, and with DR events, these pollutants are 23,14,458 kg/yr and 0.109 kg/yr for tariff-1 and 23,66,200 kg/yr for tariff-2, respectively, which are given in Table 7.30. The details about demand response events and the status of the DG, battery and other parameters under DR events for the proposed microgrid are given in Table 7.31 and Figure 7.16, respectively, which give information on DR event duration and timing and the operational performances of microgrid system components, as well as the load demand reduction under DR events. The monthly electricity charges/bill breakdown for the proposed grid-connected microgrid without and with DR events are shown in Figure 7.17. The annual savings

TABLE 7.30 Pollutant Emissions of Proposed Microgrid for Both Tariffs with DR Events Pollutant Emission

Without DR Events Tariff-1

Carbon Dioxide (kg/yr)

23,20,887

Carbon Monoxide (kg/yr)

With DR Events

Tariff-2

Tariff-1

23,70,176

18.0

23,14,458

26.0

23,66,200

18.0

Unburned Hydrocarbons (kg/yr)

0.785

1.13

0.785

Particulate Matter (kg/yr)

0.109

0.157

0.109

Sulfur Dioxide (kg/yr)

Tariff-2 21.7 0.949 0.132

10,057

10,268

10,029

10,252

4,932

5,041

4,918

5,030

Nitrogen Oxides (kg/yr)

TABLE 7.31 Demand Response Events with DG and Battery Status for Proposed Microgrid Parameters

Event-1 Start: 15 Mar 03:00 p.m.

Event-2 Start: 17 Apr 02:00 p.m.

Event-3 Start: 16 Aug 02:00 p.m.

Event-4 Start: 08 Oct 03:00 p.m.

Event-5 Start: 27 Nov 02:00 p.m.

Event-6 Start: 24 Dec 01:00 p.m.

Value

Value

Value

Value

Value

Value

Duration (hrs)

3

3

3

3

3

3

Optimized demand reduction bid (kW)

1120

1180

1180

922

996

911

Demand reduction revenue (₹)

39,239

41314

41195

32274

34869

31901

Diesel generator operating hours

0

0

0

0

0

0

Battery throughput (kWh)

287

242

390

322

329

165

Battery wear cost (₹)

55.4

46.7

75.3

62.1

63.6

31.8

Utility Tariff Variation and Demand Response Events

171

FIGURE 7.16  Implemented demand response events for proposed grid-connected microgrid.

cost summary and different cast flows during the lifetime of the proposed microgrid system, including DR events (with and without DR), are illustrated in Figures 7.18 and 7.19. In Figure 7.18, the chart summarizes the estimated annual savings in the following categories: • Demand: Savings from demand charge reduction • Energy: Consumption reduction and self-consumption • Outages: Cost savings related to utility service interruptions

172

Microgrids

FIGURE 7.17  Monthly electricity bill breakdown for proposed grid-connected microgrid: (a) without DR events, (b) with DR events.

FIGURE 7.18  Annual savings cost summary for proposed grid-connected microgrid: (a) without DR events, (b) with DR events.

FIGURE 7.19  Different cash flow during the lifetime of the proposed microgrid system: (a) without DR events, (b) with DR events.

Utility Tariff Variation and Demand Response Events 173

174

Microgrids

• O&M: Operating and maintenance costs of the proposed components • Replacements: Cost to replace proposed system components over the project lifetime • Other: Cost differences not included in the other categories • Total: The total savings (annualized) of the proposed system The different system component output power profiles and utility bill comparison with the base system and proposed microgrid system without and with DR events are illustrated in Figures 7.20 and 7.21, which clearly show the detailed performance of different system component output power profiles throughout the year. The monthly peak day profiles of proposed microgrid components, including DR events (with and without DR events), are demonstrated in Figures 7.22 and 7.23, which show the overall performance of the proposed grid-connected microgrid system with the effect of DR events in different operating conditions.

1500

AC Primary Load Total Renewable Power Output Diesel Generator Power Output

Grid Purchases Generic flat plate PV Power Output

1250

kW

1000 750 500 250 0

(a)

1500

AC Primary Load Total Renewable Power Output Diesel Generator Power Output

Grid Purchases Generic flat plate PV Power Output

1250

kW

1000 750 500 250 0

(b)

FIGURE 7.20  Different system component output power profiles for the proposed gridconnected microgrid: (a) without DR events, (b) with DR events.

175

Utility Tariff Variation and Demand Response Events Monthly Utility Bills

Current Case Proposed Case

Monthly Utility Bills

Current Case Proposed Case

2,160,000

2,005,714

2,005,714

1,851,429

1,851,429 Cost (₹)

Cost ( )

2,160,000

1,697,143

1,697,143

1,542,857

1,542,857

1,388,571

1,388,571

1,234,286

1,234,286

(a)

(b)

FIGURE 7.21  Monthly peak day profiles of proposed microgrid components with DR events.

FIGURE 7.22  Monthly peak day profiles of proposed microgrid components without DR events.

176

Microgrids

FIGURE 7.23  Monthly peak day profiles of proposed microgrid components with DR events.

The comparative summary between the base case system and best feasible solution for the proposed grid-connected microgrid system for the NITD system can be presented on the basis of the minimum net present cost and cost of energy, which clearly indicates that the net present cost of the proposed microgrid connected to the grid system is significantly reduced to ₹ 23,33,92,754 with DR and ₹ 23,71,30,140 without DR, while it was ₹ 31,78,10,200 for the base case system. Also, the cost of energy of the system decreases noticeably, from ₹ 5.76/kWh (for the base system) to 4.24 ₹/kWh with DR and 4.30 ₹/kWh without DR for the proposed microgrid system, respectively. Similarly, the total annual utility bill savings and demand charge savings for the proposed grid-connected microgrid system are significantly reduced as compared to the base system. Pollutant emissions like CO2 and particulate matter for the proposed microgrid system are considerably decreased, to 23,70,176 kg/yr and 0.157 kg/yr without DR and 23,66,200 kg/yr and 0.132 kg/yr with DR, respectively, compared to base case system. The comparison of utility bills and the cash flow of net present cost during the project lifetime for the current/base system and the proposed grid-connected microgrid system confirms that the proposed microgrid system has significant impacts on base system performance considering DR events for different tariff schemes of utilities. Finally, overall performance is enhanced with the proposed grid-connected microgrid system.

Utility Tariff Variation and Demand Response Events

177

7.6 CONCLUSIONS The proposed grid-connected microgrid system design consisting of solar PV, battery storage and diesel generator connected with the DVC-NITD-grid is analyzed based on various system performances. The resulting analysis of the proposed case study is divided into two cases, the base case system and the proposed grid-connected microgrid system, which are scrutinized using HOMER-grid for two options of tariff by the utility considering demand response events. The base case system or current system of NITD presently spends approximately ₹ 2,39,99,581.82 on the utility bill annually in which ₹ 57,23,737.24 is demand charges, approximately 24% of the utility bill. Also, the total energy purchasing charges are ₹ 1,82,75,844.59, which is a major share of the total utility bill. The proposed grid-connected microgrid system reduces the system annual utility bill to ₹ 1,76,80,120.03 without DR events and ₹ 1,74,42,214.90 with DR events by considering tariff-2 as compared to the base case system. Correspondingly, the demand and energy purchase charges are also reduced with the proposed microgrid system compared to the base system. The superior design of the proposed grid-connected microgrid is obtained with tariff-2 for both conditions of DR events. As a result of that, the net present cost of the proposed gridconnected microgrid system is significantly reduced to ₹ 23,33,92,754 with DR and ₹ 23,71,30,140 without DR as compared to the base case system (i.e., ₹ 31,78,10,200). Also, the cost of energy of the system is decreased noticeably from ₹ 5.76/kWh (for base system) to 4.24 ₹/kWh with DR and 4.30 ₹/kWh without DR for the proposed microgrid system. Similarly, the total annual utility bill savings and demand charge savings for the proposed grid-connected microgrid system are significantly reduced compared to the base system. Pollutant emissions like CO2 and particulate matter for the proposed microgrid system are considerably decreased to 23,70,176 kg/yr and 0.157 kg/yr without DR  and 23,66,200 kg/yr and 0.132 kg/yr with DR, respectively, from the base case system. The comparative analysis of utility bills and the net present cost cash flow during the project lifetime for the current system and proposed gridconnected microgrid system confirms that the proposed microgrid system has noteworthy positive influences on system performances. The reliability and resiliency of the proposed grid-connected microgrid system are enhanced compared to the base system while considering short outages for reliability and long outages for resilience for both tariffs and demand response. Also, in the proposed grid-connected microgrid system, there are more options to supply the load demand efficiently; hence, system reliability and resilience are enhanced. The required DG operation hours and net grid energy purchases are reduced; therefore, pollutant emission components such as CO2 and particulate matter are decreased for the proposed microgrid system. The lower pollutant emissions decrease the environmental and social impacts and provides for better human life regarding the bad impacts of pollutants on health. Finally, it is concluded that the proposed design of the grid-connected microgrid system is more feasible with respect to the base or current system of the case study for both conditions of DR events with tariff-2, and the overall performance of the proposed system is improved compared to the base system.

178

Microgrids

7.7 ACKNOWLEDGMENTS First, the authors would like to give thanks to NITD for helping and providing the facility to conduct research on the NITD system. Moreover, the authors also would like to acknowledge HOMER Energy for the HOMER-grid software and the NASA Prediction of Worldwide Energy Resource (Power) database to obtain the solar data of steady locations.

REFERENCES 1. Paterakis, N.G., Erdinç, O. and Catalão, J.P., 2017. An overview of demand response: Key-elements and international experience. Renewable and Sustainable Energy Reviews, 69, pp. 871–891. 2. Chai, Y., Xiang, Y., Liu, J., Gu, C., Zhang, W. and Xu, W., 2019. Incentive-based demand response model for maximizing benefits of electricity retailers. Journal of Modern Power Systems and Clean Energy, 7(6), pp. 1644–1650. 3. He, L., Zhang, S., Chen, Y., Ren, L. and Li, J., 2018. Techno-economic potential of a renewable energy-based microgrid system for a sustainable large-scale residential community in Beijing, China. Renewable and Sustainable Energy Reviews, 93, pp. 631–641. 4. Diou, C., Chrysopoulos, A., Symeonidis, A.L. and Mitkas, P.A., 2016. Response modeling of small-scale energy consumers for effective demand response applications. Electric Power Systems Research, 132, pp. 78–93. 5. Aghaei, J., Alizadeh, M.I., Siano, P. and Heidari, A., 2016. Contribution of emergency demand response programs in power system reliability. Energy, 103, pp. 688–696. 6. Tang, C.J., Dai, M.R., Chuang, C.C., Chiu, Y.S. and Lin, W.S., 2014. A load control method for small data centers participating in demand response programs. Future Generation Computer Systems, 32, pp. 232–245. 7. Babar, M., Ahamed, T.I., Al-Ammar, E.A. and Shah, A., 2013. A novel algorithm for demand reduction bid based incentive program in direct load control. Energy Procedia, 42, pp. 607–613. 8. Masrur, H., Howlader, H.O.R., Elsayed Lotfy, M., Khan, K.R., Guerrero, J.M. and Senjyu, T., 2020. Analysis of techno-economic-environmental suitability of an isolated microgrid system located in a remote Island of Bangladesh. Sustainability, 12(7), p. 2880. 9. Safamehr, H. and Rahimi-Kian, A., 2015. A  cost-efficient and reliable energy management of a micro-grid using intelligent demand-response program. Energy, 91, pp. 283–293. 10. Suresh, V., Muralidhar, M. and Kiranmayi, R., 2020. Modelling and optimization of an off-grid hybrid renewable energy system for electrification in a rural areas. Energy Reports, 6, pp. 594–604. 11. Mazzola, S., Astolfi, M. and Macchi, E., 2016. The potential role of solid biomass for rural electrification: A techno economic analysis for a hybrid microgrid in India. Applied Energy, 169, pp. 370–383. 12. Tudu, B., Mandal, K.K. and Chakraborty, N., 2019. Optimal design and development of PV-wind-battery based nano-grid system: A  field-on-laboratory demonstration. Frontiers in Energy, 13(2), pp. 269–283. 13. Zhang, D., Evangelisti, S., Lettieri, P. and Papageorgiou, L.G., 2015. Optimal design of CHP-based microgrids: Multiobjective optimisation and life cycle assessment. Energy, 85, pp. 181–193. 14. Kumar, P. and Palwalia, D.K., 2018. Feasibility study of standalone hybrid windPV-battery microgrid operation. Technology and Economics of Smart Grids and Sustainable Energy, 3(17), pp. 1–16.

Utility Tariff Variation and Demand Response Events 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.

179

García, P., Torreglosa, J.P., Fernández, L.M., Jurado, F., Langella, R. and Testa, A., 2016. Energy management system based on techno-economic optimization for microgrids. Electric Power Systems Research, 131, pp. 49–59. Thomas, D., Deblecker, O. and Ioakimidis, C.S., 2016. Optimal design and technoeconomic analysis of an autonomous small isolated microgrid aiming at high RES penetration. Energy, 116, pp. 364–379. Sawle, Y., Gupta, S.C. and Bohre, A.K., 2018. Review of hybrid renewable energy systems with comparative analysis of off-grid hybrid system. Elsevier, Renewable and Sustainable Energy Reviews, 81, pp. 2217–2235, 2017. Guo, L., Wang, N., Lu, H., Li, X. and Wang, C., 2016. Multi-objective optimal planning of the stand-alone microgrid system based on different benefit subjects. Energy, 116, pp. 353–363. Elavarasan, R.M., 2019. The motivation for renewable energy and its comparison with other energy sources: A review. European Journal of Sustainable Development Research, 3(1), pem0076(1–19). Babatunde, O.M., Munda, J.L. and Hamam, Y., 2020. A comprehensive state-of-the-art survey on hybrid renewable energy system operations and planning. IEEE Access, 8, pp. 75313–75346. HOMER energy. HOMER grid v1.8 user manual, 2020. www.homerenergy.com/pro ducts/grid/docs/1.8/design.html. Go, R., Kahrl, F. and Kolster, C., 2020. Planning for low-cost renewable energy. The Electricity Journal, 33(2), p. 106698(1–5). Fan, X., Sun, H., Yuan, Z., Li, Z., Shi, R. and Razmjooy, N., 2020. Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Reports, 6, pp. 325–335. Bohre, A.K., Agnihotri, G. and Dubey, M., 2016. Optimal sizing and sitting of DG with load models using soft computing techniques in practical distribution system. IET Generation, Transmission & Distribution, 10(6), pp. 1–16. Dali, M., Belhadj, J. and Roboam, X., 2010. Hybrid solar–wind system with battery storage operating in grid-connected and standalone mode: Control and energy management—experimental investigation. Energy, 35(6), pp. 2587–2595. Saib, S. and Gherbi, A., 2015, May. Simulation and control of hybrid renewable energy system connected to the grid. In 2015 5th International Youth Conference on Energy (IYCE), IEEE, pp. 1–6. Khalid, M., AlMuhaini, M., Aguilera, R.P. and Savkin, A.V., 2018. Method for planning a wind–solar–battery hybrid power plant with optimal generation-demand matching. IET. Renewable Power Generation, 12(15), pp. 1800–1806. DVC: Tariff order of DVC for the years 2015–16 and 2017–18. http://wberc.gov.in/sites/ default/files/Tariff%20Order_DVC_2014-2017_pdf%20%281%29.pdf.

8

SOS-Based Load Frequency Controller Design for Microgrids Using Degrees-of-Freedom PID Controller Sunita Pahadasingh, Chitralekha Jena, and Chinmoy Kumar Panigrahi

CONTENTS 8.1 Introduction................................................................................................... 181 8.2 Proposed Model............................................................................................. 183 8.2.1 Three-Area System............................................................................ 183 8.2.2 Controller Design............................................................................... 186 8.2.2.1 PID Controller..................................................................... 186 8.2.2.2 Two-Degrees-of-Freedom Controller................................. 187 8.2.2.3 Three-Degrees-of-Freedom Controller............................... 188 8.2.3 Symbiotic Organism Search Algorithm............................................ 189 8.3 Simulation Results......................................................................................... 189 8.4 Conclusion..................................................................................................... 197 8.5 Appendix....................................................................................................... 198 References............................................................................................................... 198

8.1 INTRODUCTION The control to achieve zero steady-state errors for tie lines and keep system frequency to its nominal value is the major challenge in an interconnected power system. During continuous load fluctuations, primary control by a seed governor balances active power generation but is not suitable for controlling frequency. Hence, to keep system frequency closer to minimal values, an improved control technique called load frequency control is analyzed. The concept of LFC was originated by Concordia [1]. In DOI: 10.1201/9781003121626-8181

182

Microgrids

the past, a large amount of research was done for single-area and interconnected LFC systems. Most studies discussed thermal unit generation. A single-area LFC system was proposed by Wang et  al. [2]. A  two-area interconnected thermal system was developed by Elgard and Fossa [3]. A three-area unequal thermal plant was discussed by Debbarma et al. [4], and Rahman et al. explored four-area unequal thermal plants [5]. In [6], interconnected two-area thermal hydro-plants were elaborated upon by Kothari and Nanda. Monanty et al. presented two-area interconnected thermal hydro and gas power plants [7]. A  few of these are related to LFC of an interconnected multi-area power system considering non-linearity [8] in terms of generation rate constraint (GRC) and governor dead band (GDB). GRC is a physical restraint to keep the degree of transformation of turbine power within a specified limit, generally 3% per minute, described in [9–10]. In [11], a GDB of 0.06% is reflected for the scheme and exploration drive of an LFC system. In the past decade, power generation has mostly depended on thermal and hydro power plants. With the increase in population, industries demand more power, but the availability of coal is going to be exhausted within a very short duration. As fossil fuels are exhausted with growing demand, conventional units are being integrated with distributed energy resources located closer to load centers, termed microgrids. Currently, a hybrid interconnected power system comprising conventional units and renewable energy sources like solar, wind and so on is an extensive research area in LFC [12–17]. This system diminishes transmission losses and delivers improved controllability by filling power demands. However, with irregular types of renewable sources, along with microgrids, system frequency divergences are significantly high. A microgrid could be designed in the form of distributed generation sources such as a wind turbine generator, fuel cell, aqua electrolyzer, diesel engine generator (DEG) and battery energy storage system. To sustain proper steadiness between real power and demand, a suitable control strategy is needed for power generation from these microgrid resources. Microgrid frequency deviates due to the interconnection of these energy resources. Since wind power generation is intermittent in nature, a fraction of WTG power is used for hydrogen production in AE [14–15]. The system behavior of a WTG differs from conventional thermal units due to the intermittent power curve. Hence WTGs and BESSs are harmonized to regulate the frequency eccentricities proposed by Abdul et  al. [18]. For long durations, fuel cells and fly wheels can condense frequency fluctuations, whereas energy storage devices are considered for short duration, discussed by Vidyanandan et al. [19]. BESSs [20] with multiple sources are used to soothe system frequency deviation by leveling the load with respect to generation. In [21], BESS optimization is generally used as primary reserve for minimum frequency deviation in microgrid sources. BESS discharges if the system frequency is higher than the scheduled value and charges at a state of lower value as compared to the nominal value. Many researchers have already discussed conventional controllers [22] such as proportional integral (PI) and proportional integral derivative controllers and artificial neural fuzzy intelligence systems (ANFISs) [23]. Mostly, the PID controller is used in LFC for simplification, and fewer parameters are to be optimized [24–25]. In papers [26–28], a fuzzy logic PID control for LFC has better robustness as compared to conventional PIDs. However, such methods suffer limitations of large computational time due to the design process of membership function. In industry

SOS-Based LFC Design for Microgrids

183

applications, the PID controller is mostly considered for its simpler design and robust nature. However, during transient periods, it suffers from high settling time and large overshoot. Through multiple control loops, control action is achieved, which is basically degrees of freedom [29]. In [30–33], the concept of a 2DOF-PID controller was discussed that has improved performance characteristics likened to conventional controllers. Raju et al. [31] proposed 2DOF-PID control strategies for an LFC system. Hence, when there are more tuning knobs present in a controller, the performance of the latter is better in LFC. A  three degree-of-freedom PID controller can also be experimented with for operative use in the proposed system [34–35]. Rahman et  al. [35] describe the supremacy of the 3DOF-ID controller over 2DOF-ID and single-degree-of-freedom PIDs using the biogeography-based optimization (BBO) technique. The effect of a 3DOF-PID with a dish-Stirling solar system was discussed in [12] by Rahman et al. The addition of a DC link with an existing AC link has better stability, as discussed by Ibraheem et al. [36]. A high-voltage direct current link has less frequency oscillation and better transient stability and settles down quickly to achieve a steady state, as discussed in [36–39]. Gain parameters of secondary controllers are generally adjusted by optimization techniques. In this area, many research has implemented different optimization techniques such as biogeography-based optimization [5, 12 and 35], differential evolution (DE) [7, 11], flower pollination (FP) [13], particle swarm optimization (PSO) [14], hybrid DE-PSO [27, 28], grey wolf optimization (GWO) [24, 29], bacteria foraging optimization algorithm (BFOA) [25], cuckoo search (CS) [33], teaching learningbased optimization [26, 34, 37 and 40], salp swarm algorithm (SSA) [41], ant lion optimizer (ALO) [42] and symbiotic organism search [31, 43, 44]. For optimization of controller parameters, an objective function is necessary, which is basically in the time domain. Different performance indexes such as integral of absolute error, integral of square error, integral of time multiplied square error and integral of time multiplied absolute error (ITAE) are considered as objective functions to minimize the area control error. ITAE is mostly considered as objective function because its transient behavior is superior compared to others. The main contributions of this chapter are as follows. • Design of three area hybrid thermal and hydro power sources with microgrid energy resources applied to area1. • Secondary controllers PID, 2DOF-PID and 3DOF-PID are considered to minimize area control error, and the dynamic performances of these controllers are compared through numerous simulations. • Controller parameters are optimized using SOS, TLBO and DEPSO algorithms, and the results are compared simultaneously.

8.2 PROPOSED MODEL 8.2.1 Three-Area System In this chapter, a three-area LFC system model is used, which consists of thermal units and hydro units as multiple sources in area 1, area 2 and area 3. Here, in each control area, system non-linearities are taken, such as GDB, GRC and reheat turbine

184

Microgrids

SLP

Area1-Thermal DG

B1

1

-

R1

+

Controller

GDB

Governor

Reheat Turbine

GRC

+ +

Generator

+ -

2ΠT12 S

+ -

Tie-line Area2-Thermal

B2

1

-

R2

Controller

+

GDB

Governor

SLP Reheat Turbine

GRC

+

Generator

+ -

2ΠT23 S

+ -

Tie-line

B3

1

-

Controller

FIGURE 8.1

Area3-Hydro

SLP

R3

+

GDB

Governor

Turbine

GRC

+

Generator

+ -

2ΠT31 S

+ -

Tie-line

Representation of Simulink model of power system under study.

connected by means of AC tielines. Distributed generation resources are applied to area 1, which consists of a WTG, FC, AE, DEG and BESS. Area control error (ACE) is taken as input to each controller, and the general expression of this is given in Eqs. (8.1), (8.2) and (8.3) [45]. ACE1 = B1∆F1 + ∆P12

(8.1)

ACE 2 = B2 ∆F2 + ∆P23

(8.2)

ACE 3 = B3 ∆F3 + ∆P31

(8.3)

An impartial generating unit sited downstream of the electric distribution system at or near the end user is known as distributed generation resources of microgrid. Not only does distributed generation meet a specific customer’s energy needs, it can also support the economic operation of the transmission and distribution grid. Currently, distributed generation is used for (1) standby or emergency power, (2) cogeneration (combined heat and power), (3) peak shaving, (4) grid support or (5) standalone onsite power. In this chapter, the following distributed generation resources are discussed briefly and a model of the DGR system is shown in Figure 8.2 [42]. The overall power provided to the load from the hybrid microgrid system is expressed in Eq. (8.4). Ps = PWTG + PFC + PAE − PDEG ± PBESS

(8.4)

where Ps is total power supplied, PWTG is the output power of the wind turbine generator, PFC is the output power of the fuel cell, PAE is the output power of the aqua

185

SOS-Based LFC Design for Microgrids SLP

WTG

∆Pwtg DEG

ln1

∆Pdeg

∆Pae

AE

∆Pfc

FC

∆Pbess

+ + + -

out1

BESS

FIGURE 8.2 Model of DGR system.

electrolyzer, PDEG is the output power of the diesel generator and PBESS is the output power of the battery energy storage system. 1. Diesel engine generator: DEG consists of an IC engine to adapt fuel energy into mechanical energy and then convert mechanical energy into electrical form through the generator. Hence, the main function of the DEG is to provide good reimbursement after power failure due to technical difficulties. The transfer function of the DEG is given as C DEG (s ) =

K DEG

1 +TDEG S

(8.5)

2. Fuel cell: This device provides electric energy after conversion from chemical to electrical form through hydrogen storage. It is expected that fuel cells have higher efficiencies and lower capital cost than existing units with less emission. The transfer function of FC is given as C FC (s ) =

K FC

(8.6)

1 +TFC S

3. Wind turbine generator: This is the fastest-growing renewable energy source due to economically feasibility. The amount of kinetic energy generated depends on the air density and wind speed. Wind power is mainly 3

dependent on wind speed ( m ), which varies constantly with reference to s time. The transfer function of the wind generator is given as CWTG (s ) =

KWTG

1 +TWTG S

(8.7)

186

Microgrids

4. Aqua electrolyzer: Due to high sporadic power generation from wind and photovoltaics in the microgrid system, power vacillation increases. When the fuel cell is equipped with an aqua electrolyzer (AE), the power absorbed by the AE can be controlled to lessen power fluctuations. The transfer function of the aqua electrolyzer is C AE (s ) =

K AE

1 + TAE S

(8.8)

5. Battery energy storage system: The basic idea is to convert electricity into an electrochemical form and save it as electrolytes inside a cell. While discharging, the electrolytes react with the electrodes in the cell, and the reverse reaction generates electric current. It provides additional damping oscillations to improve the stability of the power system. The first access and higher energy density characteristics make this more effective in a storage system. The transfer function of the battery energy storage system is C BESS (s ) =

K BESS

1 + TBESS S

(8.9)

8.2.2 Controller Design 8.2.2.1 PID Controller It is mostly used in an interconnected power system due to simpler structure to minimize the area control error. The basic structure of a PID controller is shown in Figure  8.3 [24]. In this, there are three parameters to be optimized (K P , K I , K D ) . However, it has severe oscillations, with ultimate overrun and large settling times damaging the system performance.

FIGURE 8.3  Basic structure of PID controller.

187

SOS-Based LFC Design for Microgrids R(s)

+

F(s)

C(s)

U(s)

Y(s)

FIGURE 8.4  Control structure of 2DOF system.

FIGURE 8.5

Basic structure of 2DOF-PID controller.

8.2.2.2 Two-Degrees-of-Freedom Controller Control action can be achieved by multiple control loops, which are called degrees of freedom. A 2DOF controller, shown in Figure 8.4 [31], is implemented in this system, which is preferable to a PID controller in terms of dynamic response. A 2DOF-PID controller contains two control loops which are optimized for this system. Therefore, when there are more tuning knobs in a controller, the performance of the latter is better in LFC. The main application of a 2DOF controller in control system engineering is to enhance control quality by means of good set point stalking and improved disruption rejection. For a 2DOF-PID controller, F(s) and C(s) are given by Eqs. (8.10) and (8.11). F (s ) = C (s ) =

(bK (K

Pi

(bK

Pi

Pi

+CK Di N i ) s 2 + (bK Pi N i + K Ii ) s + K Ii N i + K Di N i ) s 2 + (K Pi N i + K Ii ) s + K Ii N i

+CK Di N i ) s 2 + (bK Pi N i + K Ii ) s + K Ii N i s (s + N i )

(8.10)

(8.11)

where R(s) is the reference signal; Y(s) is the measured output signal; U(s) is the output signal; C(s) is the 1DOF controller; F(s) is the pre-filter on R(s); b is the proportional set-point weighting; c is the derivative set point weighting; and Kp, Ki and Kd are the proportional, integral and derivative gain, respectively.

188

Microgrids D(s)

FFc(s) R(s)

RC(S)

-

+

C(s)

+

+ P(s)

Y(s)

FIGURE 8.6  Control structure of 3DOF system.

FIGURE 8.7

Structure of 3DOF-PID controller.

8.2.2.3 Three-Degrees-of-Freedom Controller A three-degrees-of-freedom (3DOF) controller contains three control loops: closed loop stability, modeling of responses and eradication of instabilities, where R(s) is the is the input fed by ACE signal, Y(s) is the measured output signal, D(s) is the system disturbance, P(s) is the plant model, C(s) is the 1DOF controller, RC(s) is the input reference controller and FFC(s) is the feed-forward controller. Closed loop stability is provided by C(s), and the disturbance factor D(s) can be eliminated by the feed-forward controller FFC(s). The structure of a 3DOF-PID controller is presented in Figure 8.7 [35].  C (s ) P (s ) R (s )   c   P (s ) −C (s ) P (s ) FFc (s )   (8.12) Y (s ) =   R (s ) +   D (s )  1 + c (s ) P (s )    1 +C (s ) P (s )     K pi , K Ii and K Di are the gain of the single-order degree of freedom from C(s). N i is the filter constant for derivative gain. RC(s) consists of proportional set point weighting bi and derivative set point weighting. The feed forward controller FFC(s) has gain parameter gi.

SOS-Based LFC Design for Microgrids

189

8.2.3 syMbiotic oRganisM seaRch algoRithM This algorithm is the current vigorous meta-heuristic algorithm offered by Cheng and Prayogo [43]. In this technique, no specific algorithm parameters are needed; it only mimics the symbiotic relationships among different organisms for persisting in the ecosystem. In SOS, based on population space, an ecosystem is considered and, through biological interaction among organisms, new populations can be generated. There are three phases in the SOS algorithm: mutualism, commensalism and parasitism. The flowchart of an SOS algorithm is given in Figure 8.8 [31]. 1. Mutualism phase: This phase reveals the mutual benefit symbiotic relationship between two different species. In this phase, X i and X are the j two arbitrary organisms. To enhance the chance of survival, they interact between themselves (ii) X i,new = X i + rand (0, 1) * (Xbest − Mutual _ vector * BF1 )

(8.13)

X j ,new = X j + rand (0, 1) * (Xbest − Mutualvector * BF2 )

(8.14)

where Mutual _vector =

Xi + X j 2

(8.15)

rand (0,1) is the random number and BF1, BF2 are the benefit factor within the range of 1to 2. 2. Commensalism phase: Two random organisms, X i and X from the ecoj system, are permitted to interrelate in this phase. In this communication organism, X i assists the interaction, but organism X neither assists nor j rejects the connection. The new updated value of X i is calculated X i,new = X i + rand (−1, 1) * (Xbest − X j )

(8.16)

3. Parasitism phase: In this phase, one species gets benefits from the ecosystem, and the other is actively harmed. X is selected as a host for the parasite j vector from the ecosystem. This vector tries to replace X for survival in j the ecosystem, and the fitness values of both are calculated. If the parasite vector has a better fitness value, it will kill X from the ecosystem j and take its place. If X is better, it gets immunity from the parasite vector. Now the j parasite vector will no longer be alive in the ecosystem.

8.3 SIMULATION RESULTS The three-area hybrid source LFC model is designed in MATLAB/Simulink. The microgrid is considered only in area 1. The system performance is evaluated with 1% SLP in area 1 and also for a wind turbine generator. First, the supplementary PID, 2DOF-PID and 3DOF-PID controllers are taken independently. The constraints of the PID controller are first optimized by the DEPSO, TLBO and SOS algorithms.

190

Microgrids Initialization of Ecosystem Iteration=1 Identification of test organisms Start of Mutualism phase and calculate fitness No

Yes

Modified organism better than fitness

Accepted modified and replace with previous organism

Keep previous organism

Start of Commensalism phase

No

No

Yes

Modified organism better than fitness

Accept Xi,new and replace with Xi

Keep Xi and reject Xi,new

Start of Parasitism phase

No

Parasite_vector fitter than Xj

Keep Xj and remove parasite_vector

Yes

Replace Xj with parasite_vector

i=ecosize?

Termination criterion achieved Yes

Optimal solution

FIGURE 8.8  Flowchart diagram of SOS algorithm.

191

SOS-Based LFC Design for Microgrids

The corresponding dynamic behaviors are designed and compared in Figures  8.9 and 8.10. From the tabulated data Table 8.1 and also simulation results, the dynamic behaviors of SOS based PID controller has less undershoot and overshoot as compared to other algorithms such as TLBO and DEPSO.

FIGURE 8.9  Frequency deviation using PID controller.

FIGURE 8.10  Tie-line power deviation using PID controller.

TABLE 8.1 Performance Values of PID Controller Using Different Algorithms Algorithms

Performances

∆f1 in hz

∆f2 in hz

∆f3 in hz

∆f4 in hz

∆f5 in hz

∆f6 in hz

SOSPID

Minimum

–5.338

–4.5666

–6.7907

–3.9039

–0.4790

–6.2950

TLBO-PID DEPSO-PID

Maximum

1.4127

0.4476

2.3093

3.5159

5.5446

2.0698

Minimum

–6.4816

–4.6527

–7.0166

–4.2944

–1.3595

–6.8070

Maximum

2.2035

0.4740

3.7907

4.7906

5.5979

2.9469

Minimum

–9.7230

–5.3470

–7.6769

–4.4739

–1.9072

-7.4412

Maximum

4.8107

1.3059

4.4167

5.1112

5.9469

3.4517

192

Microgrids

A comparison of the performance of different controllers, such as PID, 2DOFPID and 3DOF-PID, is done individually through numerous simulations. The gain parameters of these controllers are optimized by the SOS algorithm. The PID controller has three gain parameters, K , K i and K , which is simpler in nature. The p d 2DOF controller has five gain parameters, K , K i , K , b and c . This controller p d 1 has two extra tuning parameters, which improves the trans1 ient behavior of the system. The 3DOF-PID controller has six controller parameters, K , K i , K , b , c , and p d 1 the disturbance factor ( d ). This controller has more tuning parameters as c1ompared s to the PID and 2DOF-PID controllers; hence the stability of dynamic performances are superior compared to others shown in the simulation results (Figures 8.11–8.16). The performance values of the controllers are depicted in Table 8.2.

FIGURE 8.11  Frequency deviation for area 1.

FIGURE 8.12  Frequency deviation for area 2.

FIGURE 8.13  Frequency deviation for area 3.

SOS-Based LFC Design for Microgrids

193

FIGURE 8.14  Deviation of tie line power P12.

FIGURE 8.15  Deviation of tie line power P23.

FIGURE 8.16  Deviation of tie line power P31.

Table 8.3 shows the gain parameters of the 3DOF-PID, 2DOF-PID and PID controller optimized by the SOS algorithm. The 3DOF-PID controller has three extra tuning control parameters to enhance the dynamic stability of the system, whereas the others have two controllers. Table 8.4 gives the tabulated data of PID controller parameters optimized by the SOS, TLBO and DEPSO algorithms. A bar diagram

194

Microgrids

TABLE 8.2 Performance Values of Different Controllers Optimized by SOS Algorithm Performance Characteristics Settling time (Ts) in s

Controller

Overshoot (Osh) in pu

∆f2 in hz

∆f3 in hz

∆f4 in hz

∆f5 in hz

∆f6 in hz

3DOF-PID

4.95

5.20

6.31

7.12

8.14

9.70

2DOF-PID

5.32

6.07

7.78

7.94

10.28

10.02

PID Undershoot (Ush) in pu

∆f1 in hz

5.92

7.21

8.03

8.57

11.67

12.49

3DOF-PID

–0.1982

–0.0424

–0.0445

–2.6098

–0.1854

–0.0692

2DOF-PID

–0.3634

–0.2899

–0.2515

–5.7181

–1.3212

–1.0542

PID

–0.4115

–0.3609

–0.3712

–7.1458

–1.5278

–1.6228

3DOF-PID

0.0290

0.0166

0.0142

0.2464

0.4500

0.4114

2DOF-PID

0.1857

0.0740

0.0423

1.6444

3.2698

2.5776

PID

0.2029

0.0811

0.0721

1.9020

4.5523

3.1612

TABLE 8.3 Gain Parameters of Different Controllers Optimized by SOS Algorithm Gain parameters

3DOF-PID controller

2DOF-PID controller

PID controller

K1

0.0100

0.3260

0.7924

K2

2.0000

2.0000

2.0000

K3

0.0100

1.2173

0.6479

b1 c1

0.3315

2.0000

–

1.8953

1.2492

–

Gf1

0.0100

–

–

N1

145.3172

227.5443

–

K4

2.0000

0.0100

2.0000

K5

1.6690

2.0000

1.1671

K6

1.8404

0.9427

1.2854

b2

0.2721

0.5173

–

c2

0.0100

2.0000

–

Gf2

0.1001

–

–

N2

100.0000

300.0000

–

K7 .

2.0000

0.0172

0.4397

K8

2.0000

1.7371

1.5822

K9 .

0.0100

0.9704

1.0167

b3

0.1026

2.0000

–

195

SOS-Based LFC Design for Microgrids Gain parameters

3DOF-PID controller

2DOF-PID controller

PID controller

c3.

0100

0.0100

–

Gf3

1.1065

–

–

N3

100.0000

118.4932

–

K 10

0100

0.4217

0.1000

K 11

1651

1.7643

2.0000

K 12

0.4287

1.8982

0.1000

b4

1.7726

0.4975

–

c4 Gf4

0.0100

1.2651

–

1.1079

–

–

N4

199.4876

218.8336

–

TABLE 8.4 Gain Parameters of PID Controller Tuned by Different Algorithms Gain Parameters

SOS-PID Controller

TLBO-PID Controller

DEPSO-PID Controller

K p1

1.2086

1.8480

1.2336

K i1

0.0100

0.0447

0.0100

Kd1

0.6184

1.4159

1.4782

K p2

0.2616

0.4268

1.5953

K i2

1.7796

0.7509

1.1392

Kd 2

0.6048

0.7686

1.2427

Kp3

0.0100

1.0974

0.4492

Ki 3

0.0672

1.8517

0.4666

Kd 3

0.2782

1.5531

0.9432

Kp4 Ki 4

0.1269

0.9598

0.7392

0.4434

0.6480

1.1719

Kd 4

0.0967

1.0620

1.2311

presentation of the controller’s dynamic behaviors is shown in Figures  8.17–8.19. Also, bar diagram presentations of the SOS, TLBO and DEPSO algorithm-based PID controller are shown from Figures 8.20 to 8.21.

196

FIGURE 8.17  Comparison of settling time (Ts) for different controllers.

FIGURE 8.18  Comparison of overshoot (Osh) for different controllers.

FIGURE 8.19  Comparison of undershoot (Ush) for different controllers.

Microgrids

SOS-Based LFC Design for Microgrids

197

FIGURE 8.20  Comparison of undershoot (Ush) optimized by different algorithms.

FIGURE 8.21  Comparison of overshoot (Osh) optimized by different algorithms.

8.4 CONCLUSION In this analysis, load frequency control is accomplished by a hybrid power system comprising conventional thermal, hydro plant and energy resources of microgrid. Analysis of the performances of 3DOF-PID, 2DOF-PID and PID controllers has been given. The influential metaheuristic symbiotic organism search (SOS) technique is used for tuning the gain parameters of 3DOF-PID, 2DOF-PID and PID controllers. The SOS-optimized 3DOF-PID controller delivers greater performance than the others in terms of small overshoots, undershoots and less settling time. The SOS technique performance is superior as compared to TLBO and hybrid DEPSO. The future scope of this work is analysis of the proposed LFC system for deregulated power systems.

198

Microgrids

8.5 APPENDIX Subscript refers to area ( i ) = 1, 2 and 3; Frequency f = 60 Hz Turbine time constant of thermal unit (Tti ) = 0.3 s; Reheated turbine time constant and gain are (T ) and ( K ri ) = 10 s and 0.5 ri Gain ( K ) = 120 Hz/pu MW and time constant (T ) of generator = 20 s pi pi Power interchange among areas (Tij MW/rad) = 0.086 pu Inertia constant ( H ) = 5 s i Speed regulation parameter ( R ) = 2.4pu Hz/MW i Bias coefficient ( Bi ) = 0.425 pu MW/Hz Wind turbine generation gain ( KWTG ) = 1 Gain of aqua electrolyzer ( K AE ) = 0.002 Gain of fuel cell ( K FC ) = 0.01 Diesel engine generator gain ( K DEG ) = 0.0003 Battery energy storage system gain ( K BESS ) = −0.0003 Time constant of wind turbine (TWTG ) = 1.5 sec Time constant of AE (TAE )  = 0.5 sec Time constant of FC (TFC ) = 4 sec Time constant of DEG (TDEG ) = 2 sec Time constant of BESS ( K BESS ) = 0.1 sec Hydro turbine start time (T ) = 1 s w Gain of AC/DC link ( K hvdc ) = 1 Time constant of AC/DC link (Thvdc )  = 0.2 sec Gains of electric governor K p , K i , K  = 1, 5and 4. d

REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9.

Concordia, C., Kirchmayer, L. K.: ‘Tie-line power and frequency: part II’, AISE Trans., III-A, April 1954, 73, pp. 133–146. Wang, Y., Zhou, R., Wen, C.: ‘New robust adaptive load-frequency control with system parametric uncertainties’, IEE Proc., Gener. Transm. Distrib., 1994, 141, (3), pp. 184–190. Elgerd, O.I., Fosha, C.E.: ‘Optimum megawatt–frequency control of multiarea electric energy systems’, IEEE Trans. Power Appar. Syst., 1970, PAS-89, (4), pp. 556–563. Debbarma, S., Saikia, L.C., Sinha, N.: ‘Solution to automatic generation control problem using firefly algorithm optimized IlDμ controller’, ISA Trans., 2014, 53, pp. 358–366. Rahman, A., Saikia, L.C., Sinha, N.: ‘AGC of an unequal four-area thermal system using biogeography based optimised 3DOF-PID controller’, IET Renew. Power Gener, 2016, 10. Kothari, M.L., Nanda, J.: ‘Application of optimal control strategy to automatic generation control of a hydrothermal system’, IEE Proc., 1988, 135, (4), pp. 268–274. Mohanty, B., Panda, S., Hota, P. K.: ‘Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi source power system’, Electr. Power Energy Syst., 2014, 54, pp. 77–85. Tan, W., Chang, S., Zhou, R.: ‘Load frequency control of power system with non linearities’, IET, Gen. Trans. Dist., October 2017, 11, (17), pp. 4307–4313. Saikia, L.C., Nanda, J., Mishra, S.: ‘Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system’, Int. J. Electr. Power Energy Syst., 2011, 33, pp. 394–401.

SOS-Based LFC Design for Microgrids

199

10. Debbarma, S., Saikia, L.C., Sinha, N.: ‘Robust two-degree-of-freedom controller for automatic generation control of multi-area system’, Int. J. Elect. Power Energy Syst., December 2014, 63, pp. 878–886. 11. Sahu, R.K., Panda, S., Rout, U.K.: ‘DE optimized parallel 2-DOF PID controller for load frequency control of power system with governor dead-band nonlinearity’, Int. J. Electr. Power Energy Syst., 2013, 49, pp. 19–33. 12. Rahman, A., Saikia, L.C., Sinha, N.: ‘AGC of dish-Stirling solar thermal integrated thermal system with biogeography based optimized three degree of freedom PID controller’, IET Renew. Power Gener., 2016, 10. 13. Hussain, I., Ranjan, S., Das, D.C., et al.: ‘Performance analysis of flower pollination algorithm optimized PID controller for wind-PV-SMES-BESS diesel autonomous hybrid power system’, Renew. Energy Res., 2017, 7, (2), pp. 643–651. 14. Pandey, S.K., Mohanty, S.R., Kishor, N., et al.: ‘Frequency regulation in hybrid power systems using particle swarm optimization and linear matrix inequalities based robust controller design’, Electr. Power Energy Syst., 2014, 63, pp. 887–900. 15. Saha, D., Saikia, L.C.: ‘Performance of FACTS and energy storage devices in a multi area wind-hydro-thermal system employed with SFS optimized I-PDF controller’, Renew. Sustain. Energy, 2017, 9, p. 024103. 16. Raju, M., Saikia, L.C., Sinha, N.: ‘Load frequency control of multi-area hybrid power system using symbiotic organisms search optimized two degree of freedom controller’, Renew. Energy Res., 2017, 7, (4), pp. 1663–1674. 17. Mirazimi, J., Fathi, M.: ‘Analysis of hybrid wind/fuel cell/battery/diesel energy system under Alaska condition’, 8th Electrical Electronics Computer Telecommunications Information Technology Association (ECTI), Thailand, 2011, pp. 917–92. 18. Howlader, A.M., Izumi, Y., Uehara, A., Urasaki, N., Senjyu, T., Yona, A., Saber, A.Y.: ‘A minimal order observer based frequency control strategy for an integrated windbatterydiesel power system’, Energy, October 2012, 46, (1), pp. 168–178. 19. Vidyanandan, K.V., Senroy, N.: ‘Frequency regulation in a wind–diesel powered microgrid using flywheels and Fuel Cell’, IET Gener. Trans. Distr., 2016, 10, (3), pp. 780–788. 20. Serban, I., Marinescu, C.: ‘Battery energy storage system for frequency support in microgrids and with enhanced control features for uninterruptible supply of local loads’, Electr. Power Energy Syst., January 2015, 54, pp. 432–441. 21. Alexandre Oudalov, E., Chartouni, D., Ohler, C.: ‘Optimizing a battery energy storage system for primary frequency control’, IEEE Trans. Power Syst., 2007, 22. 22. Nanda, J., Mangla, A., Suri, S.: ‘Some new findings on automatic generation control of an interconnected hydrothermal system with conventional controllers’, IEEE Trans. Energy Convers., 2006, 21, (1), pp. 187–194. 23. Khuntia, S.R., Panda, S.: ‘Simulation study for automatic generation control of a multiarea power system by ANFIS approach’, Appl. Soft Comput., 2012, 12, pp. 333–341. 24. Guha, D., Roy, P.K., Banerjee, S.: ‘Load frequency control of interconnected power system using grey wolf optimization’, Swarm Evol. Comput., 2016, 27, pp. 97–115. 25. Ali, E.S., Abd-Elazim, S.M.: ‘BFOA based design of PID controller for two areas load frequency control with nonlinearities’, Int. J. Electr. Power Energy Syst., 2013, 51, pp. 224–231. 26. Sahu, Binod Kumar, et al.: ‘Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system’, Appl. Soft Comput., 2015, 27, pp. 240–249. 27. Pahadasingh, S., Jena, C., Panigrahi, C.K., ‘Fuzzy PID AGC of multi area power system optimized by hybrid DEPSO algorithm with FACTS’, Inter. J. Recent Technol. Eng., 2019, 8, pp. 9563–9569. 28. Nayak, R.J., Sahu, K.B.: ‘Load frequency control of hydro thermal system using fuzzy PID controller optimized by hybrid DECPSO algorithm’, Inter. J. Pure Appl. Math., August 2017, 114, pp. 147–155.

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29. Patel, N.C., Debnath, M.K., Bagarty, D.P., Das, P.: ‘GWO tuned multi degree of freedom PID controller for LFC’, Inte. J. Eng. Technol., 2018, 7, pp. 548–552. 30. Debbarma, S., Saikia, L.C., Sinha, N.: ‘Automatic generation control using two degree of freedom fractional order PID controller’, Int. J. Electr. Power Energy Syst., 2014, 58, pp. 120–129. 31. Raju, M., Saikia, L.C., Sinha, N.: ‘Load frequency control of multi-area hybrid power system using symbiotic organisms search optimized two degree of freedom controller’, Renew. Energy Res., 2017, 7, (4), pp. 1663–1674. 32. Singh, J., Chatterjee, K., Vishwakarma, C.B.: ‘Two degree of freedom internal model control-PID design for LFC of power systems via logarithmic approximations’, ISA Trans., 2018, 72, pp. 185–196. 33. Dash, P., Saikia, L.C., Sinha, N.: ‘Comparison of performances of several FACTS devices using cuckoo search algorithm optimized 2DOF controllers in multi-area AGC’, Electr. Power Energy Syst., 2015, l, (65), pp. 316–324. 34. Debnath, M.K., Satapathy, P., Mallick, R.K.: ‘3DOF-PID controller based AGC using TLBO algorithm’, Inter. J. Pure Appl. Math., 2017, 114, pp. 39–49. 35. Rahman, A., Saikia, L.C., Sinha, N.: ‘Load frequency control of a hydro-thermal system under deregulated environment using biogeography-based optimized three-degreeof-freedom integral–derivative controller’, IET Gener. Trans. Distr., 2015, 9, (15), pp. 2284–2293. 36. Ibraheem, N., Bhatti, T.S.: ‘AGC of two area power system interconnected by AC/ DC links with diverse sources in each area’, Electr. Power Energy Syst., 2014, 55, pp. 297–304. 37. Barisal, A.K.: ‘Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power systems’, Electr. Power Energy Syst., 2015, 66, pp. 67–77. 38. Arya, Y., Kumar, N., Ibraheem: ‘AGC of a two-area multi-source power system interconnected via AC/DC parallel links under restructured power environment’, Optim. Control Appl. Methods., 2016, 37, pp. 590–607. 39. Arya, Y., Kumar, N.: ‘AGC of a multi-area multi-source hydrothermal power system interconnected via AC/DC parallel links under deregulated environment’, Electr. Power Energy Syst., 2016, 75, pp. 127–138. 40. Rao, R.V., Savsani, V.J., Vakharia, D.P.: ‘Teaching–learning-based optimization: An optimization method for continuous non-linear large scale problems’, Inf. Sci., 2012, 183, (1), pp. 1–15. 41. Guha, D., Roy, P.K., Banerjee, S.: ‘Maiden application of SSA optimized CC-TID controller for LFC of power systems’, IET Gener. Trans. Distr., 2019, 13, pp. 1110–1120. 42. Raju, M., Saikia, L.C., Sinha, N.: ‘Load frequency control of a multi-area system incorporating distributed generation resources, gate controlled series capacitor along with high-voltage direct current link using hybrid ALO-pattern search optimised fractional order controller’, IET Renew. Power Gener., 2019, 13, (2), pp. 330–341. 43. Cheng, M., Prayogo, D.: ‘Symbiotic organisms search: A new metaheuristic optimization algorithm’, Inter. J. Comp. Struc., 2014, 139, pp. 98–112. 44. Guha, D., Roy, P.K., Banerjee, S.: ‘Symbiotic organism search based load frequency control with TCSC’, Proc. Fourth Int. Conf. Recent Advances in Information Technology, IIT (ISM) Dhanbad, 15–17 March 2018, pp. 1–6. 45. Kundur, P.: Power system stability and control (McGraw Hill, New York, 1994, 2nd edn.).

9

Performance Improvement of a UPQC Integrated with a Microgrid System Using Modified SRF Technique Sarita Samal, Prasanta Kumar Barik, and Prakash Kumar Hota

CONTENTS 9.1 Introduction................................................................................................... 201 9.2 Proposed System............................................................................................ 203 9.2.1 Modeling of Solar PV........................................................................ 203 9.2.2 Fuel Cell Modeling............................................................................205 9.3 UPQC Detail Model......................................................................................207 9.3.1 Control Scheme of UPQC.................................................................209 9.3.1.1 SRF Control Scheme...........................................................209 9.3.1.2 MSRF Control Scheme....................................................... 210 9.4 Results and Discussion.................................................................................. 210 9.4.1 Scenario-1 Analysis........................................................................... 211 9.4.2 Scenario-2 Analysis........................................................................... 212 9.5 Conclusion..................................................................................................... 212 References............................................................................................................... 215

9.1 INTRODUCTION Microgrid (MG) may be represented as a small-scale power system that contains loads, energy sources, energy storage units and control and protection systems [1]. Using an MG is attractive, as it improves the system quality, decreases carbon emissions and reduces losses in transmission and distribution systems [2]. MGs can be connected to the main grid or operate autonomously. When an MG is connected to utility grid, the control systems required to maintain the active and reactive power output from the energy sources connected to the MG are simple. However, under DOI: 10.1201/9781003121626-9201

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autonomous operation, the MG is disconnected from the utility grid and operates in islanded mode [3, 4]. Usually, a stand-alone MG system is used to supply power to isolated areas or places interconnected to a weak grid. The application of an MG reduces the probability of energy supply scarcity. The proposed MG consists of renewable energy source-based power sources, that is, solar PV and a fuel cell-based MG. Solar PV and wind energy are complementary in nature, and both depends upon climatic conditions; hence, to get an uninterrupted power supply at any time and maintain the continuity of the load current, one of the most-developed energy sources, like a fuel cell, is combined with these RESs [5]. However, the electric power system is mostly affected by nonlinear loads; arc furnaces, switch mode power supply, power electronic converters and household electronic equipment play a key role in polluting supply voltages and currents. The increase of power electronicbased equipment in household appliances and industries is the main cause of pollution of power systems [6]. Consequently, power quality (PQ) improvement is a major issue today. Research in the area of power electronics makes sure that unified power quality conditioner (UPQC) plays a vital role for achieving superior PQ levels. In the present scenario, the series active power filter (APF) and shunt APF alone do not meet the requirements for compensating PQ distortions. However, the UPQC is a device which consists of these two APFs integrated with the DC-link capacitor. The series APF is integrated though a series transformer and the shunt APF (SAPF) through an interfacing inductor. The series inverter acts as a voltage source, whereas the shunt acts as a current source. Simultaneous compensation of voltage and current-related PQ distortions using UPQC is achieved by proper control of series APF and SAPF. The SAPF is employed to provide compensating currents to the PCC for generation/ absorption of reactive power and harmonics suppression. Moreover, the operation of the SAPF depends upon three main parts which are important in its design; these consist of the control method used for generation of the reference current, the technique used for switching pulse generation for the inverter and the controller used for DC link capacitor voltage regulation. Different control strategies explained in literature are as follows. The typical use of SAPFs for current harmonic compensation in domestic, commercial and industrial applications was explained in Hosseinpour et al. [7]. An experimental study and simulation design of an SAPF for harmonics and reactive power compensation were explained by Montero et al. [8]. The power balance theory for active and reactive power compensation was developed by Jain et al. [9]. The instantaneous reactive power techniques of three-phase SAPF for compensation of source current harmonics were explained by Singh et  al. [10]. Sag is the most significant PQ problem faced by many industrial consumers. The control for such a case can be analyzed by protecting sensitive loads in order to preserve a load voltage without sudden phase shifts [11]. Different control strategies for series APF were analyzed by Ferdi et al. [12] with a focus on the reimbursement of voltage sags with phase jump. Different control techniques to reimburse voltage sags with phase jump were also examined and compared by Jowder et al. [13]. To ensure stable operation and improve the system performance of distributed generation in islanded mode, a comparative study of two different control techniques used in UPQC-like reference current generation, that is, the synchronous reference frame (SRF) and modified synchronous reference frame (MSRF) methods in conjunction with a pulse

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width modulation-based hysteresis band controller is proposed in this chapter using MATLAB simulation software. The PQ issues like voltage sag compensation, current and voltage harmonics are analyzed both in linear and nonlinear loads.

9.2 PROPOSED SYSTEM The projected MG system (comprising solar and fuel cell-based energy sources) is shown in Figure 9.1, where the MG system generates DC power to the DC bus, and by using a power inverter, this DC power is converted to AC. The AC bus delivers the power to the load, which may be a linear or nonlinear. The UPQC is located between the MG and nonlinear load, which manages the power quality of the system by using different control techniques.

9.2.1 Modeling of Solar PV A single diode model-based PV cell is used for the design of the MG. Figure 9.2 represents the single-diode equivalent model of the solar PV system. The basic equation for the design of the PV system is given in the following [14, 15]. (9.1)

I = I s − I d − I sh

( )

  G I S = I + K T T  × i k  1000  sc I RS

I sc

 exp (q ×V / N × k × A ×T ) − 1 OC S  

FIGURE 9.1  Proposed MG and UPQC.

(9.2) (9.3)

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FIGURE 9.2  SPV model.

FIGURE 9.3

SPV with boost converter and MPPT.

I o = I RS

3 q × E T  go   exp   T   Ak  r 

  1  1   −   T  r T 

(9.4)

 q ×V + I × R    PV PV se  I PV = N P × I Ph − N P ×IO  exp   − 1   N s × AkT     

( )

(9.5)

where R  = resistance in series Ω , Ι  = reverse saturation current of diode (A), se 0 Voc  = open circuit voltage (V) , Rsh  = shunt resistance Ω , Ιsc  = short circuit current (A) , ΙPV  = diode photo current (A), VPV  = diode voltage (V) , k = Boltzmann constant, N P   =  parallel connected cells, T   =  temperature of the p‑n junction (Kelvin), N  = cells in series, A = diode ideality factor, q = electron charge. s Based on Equations (9.1–9.5), a single-diode model solar PV cell is developed and implemented in MATLAB simulations [16–20]. Figure 9.3 shows the MATLAB simulation of PV with an MPPT and boost converter. Figure 9.4 shows its corresponding

( )

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FIGURE 9.4  Voltage output.

TABLE 9.1 Different Parameters of Solar PV Different Parameters

Ratings

Np

72

Ns

01

Isc

10.2 A

Voc

90.5 V

Vmp

81.5 V

Imp

8.6 A

V0

230 V

output voltage, where the required voltage of 230  V is achieved. The parameters required for design of the solar PV system are given in Table 9.1.

9.2.2 Fuel Cell Modeling The proton exchange membrane (PEM) FC is considered another energy source of the MG. The FC consists of two electrodes, a positive cathode and negative anode, and an electrolyte. The pressurized hydrogen gas enters as the anode of the FC and oxygen enters the cathode [21, 22]. A basic PEMFC diagram is shown in Figure 9.5, and its chemical reactions are presented in Equations (9.6–9.8) [23]. The reaction at the anode is given as: (2H+ + 2e- ) → H 2

(9.6)

The reaction at the cathode is given as: (2H+ + 2e- + 1/2O2) → H O 2

(9.7)

206

FIGURE 9.5

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FC model.

The net reaction is shown as:

H2 + 1/2O → H2O 2

E Thermo = 1.229 − 0.00085 × (T − 298.15) + 4.31 × 10−5 ×T   1 ×  ln (PH 2 ) + ln (PO 2 )   2  

(9.8) (9.9)

Vact = −  ξ1 + ξ 2 ×T + ξ3 ×T × ln (CO2 )  

(9.10)

VOhmic = iFC (RM + RC )

(9.11)

VFC = E N −Vact −VOhmic −Vcon

(9.12)

Vs = k ×VFC

(9.13)

 J  Vcon = ln 1 −  × (−B )  J max 

(9.14)

The thermodynamically predicted voltage ( E thermo ) of FC is shown in Equation (9.10) [24]. Equations (9.9)–(9.14) are required for the modeling of PEMFC in MATLAB [25]. Figures 9.6 and 9.7 represent the detailed model with output voltage. The simulation parameters of FC are presented in Table 9.2.

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FIGURE 9.6  FC with BC block diagram.

FIGURE 9.7  Voltage output.

TABLE 9.2 FC Parameters Parameters

Values

O2

59.3%

Vfc

230 V

vs H2 k

1.128 V

Rt

0.70833 Ω

Rl

99.56% 65 5Ω

9.3 UPQC DETAIL MODEL This section gives the system configuration and detailed description of the UPQC. The basic structure of the UPQC is shown in Figure 9.8, and it consists of two inverters connected to a common DC-link capacitor. The series inverter is connected

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FIGURE 9.8  Basic UPQC model.

through a series transformer, and the shunt inverter is connected in parallel with the point of common coupling. The series inverter acts as a voltage source, whereas the shunt acts as a current source. The main function of the UPQC is to control the power flow and reduce the harmonics distortion both in voltage and current waveforms. The series APF protects the load from utility-side disturbances. In the case of series APF, Park’s transformation method is used for generation of the unit vector signal. A PWM generator, generating synchronized switching pulses, is given to the six switches of the series converter. The objectives of the series converter can be achieved by providing appropriate switching pulses. The shunt active power filter injects the compensating current to the PCC such that the load current becomes harmonics free. With the aid of the voltage source inverter and interfacing inductor, the compensating current is generated, which minimizes the harmonics component in the load current. The maximum di/dt that can be accomplished by the inverter is determined by the voltage across the interfacing inductor. This is significant because a higher di/dt is essential to reimburse the harmonics; hence, the selection of the interfacing inductor is vital. However, the ability of the compensator depends upon the interfacing inductor, where a higher size can compensate higher-order harmonics. The SAPF generates a compensating current, which is in opposition to the harmonic current generated by the nonlinear load. This compensating current cancels out the current harmonics and makes the load current sinusoidal. So, the SAPF is used to eradicate current harmonics and reimburse reactive power at the source side so as to make the load current harmonics free. Equations (9.15) and (9.16) show the instantaneous current and the source voltage, respectively. Ic (t ) = IL (t ) - Is (t ) 

(9.15)

Vs (t ) = Vm sin ωt 

(9.16)

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Is (t ) = I 1Sin (ωt + Φ ) + Σn =2InSin (n ωt + Φn )

(9.17)

Is (t ) = IL (t ) - IC (t )

(9.18)

1

ε

The instantaneous value of the source, load and compensation current can be expressed by Is (t ) , IL (t ) and IC (t ) , where Vs (t ) and Vm correspond to the instantaneous value and peak value of the source voltage.

9.3.1 contRol scheMe of upqc There are many control methods available for SAPF. Here, the following two basic design methods, which are easy to implement, are considered. • Synchronous reference frame method • Modified-SRF method 9.3.1.1 SRF Control Scheme The normal arrangement of an SRF system contains a phase-locked loop (PLL) unit for vector orientation, as shown in Figure 9.9. The control pattern includes the transfer of source current from abc to d‑q. The three-phase load currents Ila , Ilb, Ilc are converted to Id–Iq using the transformation technique, as given in Equation (9.19).      i  + cos θ cos θ 120 cos θ 120 − ( ) ( )   ila  q  i  = 2 sin θ sin θ − 120 sin θ + 120  i  ( ) ( )   lb   d   3   ilc  1 1 1 io      2  2 2

FIGURE 9.9  Block diagram of SRF control scheme.

(9.19)

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9.3.1.2 MSRF Control Scheme Figure 9.10 shows the block diagram of the MSRF scheme, where, instead of PLL, a unit vector generation method is used. Figure 9.11 shows a block diagram to generate the unit vector by sensing the supply voltage. cosθ =

Vα

(V 2) + (V 2) sα

sinθ =

(9.20)

sβ

Vβ

(V 2) + (V 2) sα

(9.21)

sβ

9.4 RESULTS AND DISCUSSION The proposed model is tested based on following scenarios: Scenario-1: MG connected to nonlinear load without UPQC and UPQC with SRF technique. Scenario-2: MG with MSRF-based UPQC.

FIGURE 9.10  MSRF scheme.

FIGURE 9.11  Unit vector generation model.

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9.4.1 Scenario-1 Analysis In this case, the system performance is analyzed by connecting a non-linear load with the MG system first without UPQC and then with SRF-based UPQC. The performance of series APF can be evaluated by introducing voltage sag into the system. The profile of load voltage shown in Figure 9.12(a) confirms that voltage sag is introduced from 0.1 to 0.3 s of the load voltage waveform. For the sag condition, the series APF detects the voltage drop and injects the required voltage through the series coupling transformer. It maintains the rated voltage across the load

FIGURE 9.12  Profile obtained under Scenario-1 (sag compensation): (a) load voltage before compensation, (b) compensating voltage injected by UPQC and (c) load voltage after compensation.

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terminal. In order to compensate the load voltage sag, UPQC (employing an SRF scheme) is turned on, which injects compensating voltage at the PCC, as displayed in Figure 9.12(b); as a result, the load voltage is same as the source voltage. The load voltage after compensation is shown in Figure 9.12(c). In general, the operation of the series part of the UPQC can be described as rapid detection of voltage variations at the source, and it injects the compensation voltage, which maintains the rated voltage across the load terminal. The shunt voltage source inverter in the UPQC is realized as a shunt APF and is applied to solve the current-related PQ distortion, current harmonic distortion, reactive power demand and so on. In order to investigate the performance of the shunt APF, a rectifier-based nonlinear load is introduced into the system, and the level of harmonics is checked. It is observed from Figure 9.13(a) that the source current waveform has a total harmonic distortion (THD) of 16.60%, per the fast Fourier transform (FFT) analysis of the source current, as shown in Figure 9.13(b). In order to make the source current sinusoidal, the shunt APF of the UPQC with the conventional SRF technique is turned on at t = 0.1 s, which injects a compensating current as displayed in Figure 9.13(c). Hence, the THD level comes down to 3.40%, as shown in Figure 9.13(d).

9.4.2 Scenario-2 Analysis It is observed from Scenario-1 that an MG connected with the SRF method mitigates the harmonics only by 3.40%. Hence, the PQ analysis of the MG system by utilizing the MSRF scheme is studied in this case. As the main objective of the research is to compensate sag, decrease harmonics and maintain the DC link capacitor voltage, the investigation is focused on series APF and shunt APF of the UPQC. The voltage injected by the proposed controller, shown in Figure 9.14(a), reduces the sag from 0.1 to 0.3 s and makes the load voltage equal to the source voltage. The load voltage after compensation is presented in Figure  9.14(b). The profile of the compensating current generated by the MSRF-based shunt APF and source current after compensation are shown in Figures 9.14(c) and 9.14(d), respectively. It may be seen from Figure 9.14(d) that the source current is sinusoidal in nature due to the injection of compensating current by the UPQC. FFT analysis of the source current (after compensation) is presented in Figure 9.14(e) and ensures further reduction of THD content to 2.54% in comparison to Scenario-1. The simulation is carried out for a non-linear load with these controllers and makes the source current almost sinusoidal after compensation. FFT analysis of the proposed method confirms that the THD of the source current is in compliance with IEEE-519 harmonic standards.

9.5 CONCLUSION The investigation shows that that MSRF technique of UPQC makes it possible to lighten the PQ of a MG system connected with a nonlinear load. Moreover, the proposed system also enhances the performance of the UPQC by attaining a lower THD value of the source current and voltage sag compensation. From the numerical comparison of results, it is noticeable that the MSRF-based UPQC can reduce the source

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FIGURE 9.13  Profile obtained under Scenario-1 (harmonics mitigation): (a) source current before compensation, (b) harmonics content before compensation, (c) compensating current injected by UPQC and (d) harmonics content after compensation.

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FIGURE 9.14  Profile obtained under Scenario-2: (a) compensating voltage injected by series APF of UPQC, (b) load voltage after compensation, (c) compensating current injected by shunt APF of UPQC, (d) source current after compensation and (e) harmonics content after compensation.

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current harmonics about 2.54%. Investigations may be made into using a hybrid filter and designing its control loop in order to improve the power quality of the system in various aspects.

REFERENCES 1. 2.

3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

Badoni, M.; Singh, B.; Singh, A.: Implementation of echo-state network-based control for power quality improvement, IEEE Transactions on Industrial Electronics,  2017, Vol.64(7), pp. 5576–5584. Rabiee, A.; Sadeghi, M.; Aghaeic, J.; Heidari, A.: Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties, Renewable and Sustainable Energy Reviews, 2016, Vol.57, pp. 721–739. Mahmoud, M.S.; Rahman, M.S.U.; Fouad, M.S.: Review of microgrid architectures— a system of systems perspective,  IET Renewable Power Generation, 2015,  Vol.9(8), pp. 1064–1078. Samal, S.; Hota, P.K.: Design and analysis of solar PV-fuel cell and wind energy based microgrid system for power quality improvement, Cogent Engineering, 2017, Vol.4(1), pp. 1402453. Suresh, M.; Patnaik, S.S.; Suresh, Y.; Panda, A.K.: Comparison of two compensation control strategies for shunt active power filter in three-phase four-wire system, Innovative Smart Grid Technologies (ISGT), IEEE PES, 2011, pp. 1–6. Tang, Y.; Loh, P.C.; Wang, P.; Choo, F.H.; Gao, F.; Blaabjerg, F.: Generalized design of high performance shunt active power filter with output LCL filter, IEEE Transactions on Industrial Electronics,2012, Vol.59(3), pp. 1443–1452. Hosseinpour, M.; Yazdian, A.; Mohamadian, M; Kazempour, J.: Design and simulation of UPQC to improve power quality and transfer wind energy to grid, Journal of Applied Sciences, 2008, Vol.8(21), pp. 3770–3782. Montero, M.I.M.; Cadaval, E.R.; Gonzalez, F.B.: Comparison of control strategies for shunt active power filters in three-phase four-wire systems, IEEE Transactions on Power Electronics, 2007, Vol.22(1), pp. 229–236. Samal, S.; Hota, P.K.; Barik, P.K.: Performance improvement of a distributed generation system using unified power quality conditioner.  Technology and Economics of Smart Grids and Sustainable Energy, 2020, Vol.5(1), pp. 1–16. Singh, B.N.; Singh, B.; Chandra, A.; Al-Haddad, K.: Design and digital implementation of active filter with power balance theory, IEE Proceedings-Electrical Power Application, 2005, Vol.152(5), pp. 1149–1160. Dixon, J.W.; Venegas, G.; Moran, L.A.: A  series active power filter based on a sinusoidal current-controlled voltage-source inverter,  IEEE Transactions on Industrial Electronics, 1997, Vol.44(5), pp. 612–620. Ferdi, B.; Benachaiba, C.; Dib, S.; Dehini, R.: Adaptive PI control of dynamic voltage restorer using fuzzy logic, Journal of Electrical Engineering: Theory & Application, 2010, Vol.1(3), pp. 165–175. Jowder, F.A.L.: Design and analysis of dynamic voltage restorer for deep voltage sag and harmonic compensation,  IET Generation, Transmission  & Distribution,  2009, Vol.3(6), pp. 547–560. Akagi, H.; Kanazawa, Y.; Nabae, A.: Instantaneous reactive power compensators comprising switching devices without energy storage components, IEEE Transactions on Industrial Electronics Application, 1984, Vol.20(3), pp. 625–630. Barik, P.K.; Shankar, G.; Sahoo, P.K.: Power quality assessment of microgrid using fuzzy controller aided modified SRF based designed SAPF, International Transactions on Electrical Energy Systems, 2019, p. e12289. https://doi.org/10.1002/2050-7038.12289.

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16. Hatziargyriou, C.S.; Liang, T.: The microgrids concept, microgrid: Architectures and control, John Wiley-IEEE Press, Chichester, 2014, pp. 1–24. 17. Olivares, D.E., et al.: Trends in microgrid control, IEEE Transactions on Smart Grid, 2014, Vol.5(4), pp. 1905–1919. 18. Altas, I.H.; Sharaf, A.M.: A  photovoltaic array simulation model for MATLABSimulink GUI environment, Clean Electrical Power, ICCEP’07, pp. 341–345. 19. Femia, N.; Petrone, G.; Spagnuolo, G.; Vitelli, M.: Optimization of perturb and observe maximum power point tracking method, IEEE Transactions on Power Electronic, 2007, Vol.20(4), pp. 963–973. 20. Samal, S.; Hota, P.K.; Barik, P.K.: Harmonics mitigation by using shunt active power filter under different load condition, 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), October 2016, pp. 94–98. 21. Kalirasu, A.; Dash, S.S.: Simulation of closed loop controlled boost converter for solar installation, Serbian Journal of Electrical Engineering, 2010, Vol.7(1), pp. 121–130. 22. Noroozian, R.; Abedi, M.; Gharehpetian, G.B.; Bayat, A.: On-grid and off-grid operation of multi-input single-output DC/DC converter based fuel cell generation system, Electrical Engineering (ICEE), 18th Iranian Conference,2010, pp. 753–758. 23. Samal, S.; Hota, A.; Hota, P.K.; Barik, P.K.: Harmonics and voltage sag compensation of a solar PV-based distributed generation using MSRF-based UPQC, Innovation in Electrical Power Engineering, Communication, and Computing Technology, Lecture Notes in Electrical Engineering, vol. 630, Springer, 2020.

10

Application of Probabilistic Neural Network and Wavelet Analysis to Classify Power Quality Pampa Sinha and Chitralekha Jena

CONTENTS 10.1 Introduction................................................................................................. 217 10.2 Proposed Method......................................................................................... 218 10.3 System Description...................................................................................... 220 10.4 Wavelet Transform-Based Feature Extraction............................................. 221 10.4.1 Capacitor Switching....................................................................... 221 10.4.2 Motor Switching............................................................................. 222 10.4.3 Fault-Induced Events...................................................................... 223 10.4.4 Converter Switching.......................................................................224 10.4.5 Transformer Switching................................................................... 225 10.5 Categorization of Transients Using PNN.................................................... 226 10.6 Comparative Analysis.................................................................................. 228 10.7 Conclusions.................................................................................................. 229 References............................................................................................................... 229

10.1 INTRODUCTION One of the approaches used to identify the source of power quality problems in power systems is to use classifiers. Classifiers utilize the characterizing features of various forms of power system signals in order to determine the different types and disturbances. Of the various forms of classifier tools, neural networks have been used for some time. In this chapter, the use of a specific variety of neural network, a probabilistic neural network (PNN), is investigated for the categorization and identification of power quality problems. Transients in power systems are a challenge for the coordination of equipment insulation. Transients occurring due to different DOI: 10.1201/9781003121626-10217

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types of switching have typical characterizing frequencies. Wavelet transform can be effectively employed to extract the features contained in these transient signals. In this chapter, a neural network model based on wavelet transform was designed for transient classification due to capacitor, motor, converter, transformer switching or fault. For these five transients, detailed aspects of reactive powers are found, and the proposed model uses reactive power as input to the PNN, which is used to categorize the different transients. In this chapter, an 11 kV distribution system is considered. The transient signals are successfully classified using a neural network. Various combinations of feature extraction and classification methods have been reported for identification of different problems related to power quality. In [1], wavelet transform was employed as a means for supervising the problems generated due to the dynamic performance of industrial plants. The authors of [2] proposed a waveletbased NN model for identifying power quality problems, which was executed and tested with a number of transient events. For developing a classifier, discrete wavelet transform (DWT) is used with the PNN model. In [3], the authors proposed a method to distinguish transients arising. A  DWT of the nodal voltage signal was applied to find typical properties of the voltage waveform. The authors of [4] presented a new approach for meticulous differentiation between magnetizing inrush current and internal fault in a power transformer by considering wavelet transforms with NNs. The authors of [5] employed sparse representation classification (SRC) in addition to a feature extraction procedure using dual-tree complex wavelet transform (DTCWT). The authors of [6] proposed a DTCWT for identification of power system transients. An S-transform-based competitive neural network (CNN) classifier was described in [7] for recognition of inrush current. The authors of [8] reviewed the literature for power quality on existing applications of advanced artificial intelligence approaches. It includes an extended accumulation of literature considering fuzzy logic applications and neural networks as well as genetic algorithms in power quality. A PNN is utilized for classifying transients related to power systems. In experiments for power transients, it has been shown that the classification system achieves a 99% classification rate and is robust in noisy environments. A  method of utilizing PNN in the dynamic security assessment of power systems was proposed in [9]. The authors of this chapter presented different transients like motor or capacitor switching, various fault-induced transients and transformer and converter inrush transients utilizing PNN approaches. To find the exceptional characteristic of each transient, first, the DWT of the signal is utilized for the calculation of the reactive power at levels 1 and 3. As the ANN has the possibility to simulate the learning process as well as working as a classifier, a PNN is used for classifying the stated disturbances in transients, and it is verified that this single-point approach is very efficient for classification of transient signals.

10.2 PROPOSED METHOD A novel method is proposed in this chapter, which is based on PNN and DWT for categorization of motor, converter, capacitor or transformer switching as well as faultinduced events. A flowchart is shown in Figure 10.1 to describe the proposed method

Application of PNN and Wavelet Analysis to Classify Power Quality

FIGURE 10.1  Flowchart for classification of different transients.

219

220

Microgrids

for categorization of different transients. A PNN is chosen as a classifier to distinguish the transient signals. Every input signal is created with 128 samples per cycle; 0.1 second is the capturing time and consequently 6.4 kHz the sampling frequency. DWT is used for calculation of reactive power details at the sending end at levels 1 (Qdet1) and 3 (Qdet3) using the equation, Qdet =

‘1 2p

j

2 0 −1

∑

d

k =0

j 0,k

d “j 0, k 

These reactive powers are utilized as the input to the PNN model for classification of transients. In this method, motor switching, capacitor switching, fault, converter and transformer switching are considered classes 1, 2, 3, 4 and 5, respectively, during training of the PNN. So according to the input, this algorithm will display the corresponding class of the transient signal. The reliability and validity of numerous case studies have been tested, which indicates that this method is ideal for classifying transients.

10.3 SYSTEM DESCRIPTION In Figure 10.2, a radial distribution network is considered. For each section ZS, the internal impedance of the voltage source and the line impedance are taken as (0.5 + j0.5) Ω and (1 + j1) Ω, respectively. Each load impedance, that is, Z1, Z2, Z3, Z4and Z5 is (300 + j300) Ω. The transformer, capacitor, motor and converter are joined to timecontrolled switches to every bus to get detailed reactive power under diverse transient conditions. Electromagnetic transient program (EMTP) is implemented to get the instantaneous current and voltage signals from the sending end, which is shown in Figure 10.2. The signals are analyzed using DWT with MATLAB. 11 kV distribution systems are modeled. Figure  10.2 shows the EMTP model. The capturing time is 0.1 second, and 6.4 kHz is the sampling frequency, that is, 128 samples per cycle. A Daubechies wavelet with four filter coefficients (db4) is used for feature extraction. Simulation of all five transients is performed. The switching transients are classified by the PNN. The original signal is decayed up to level 5 with discrete wavelet transform, and then after consideration of all the parameters, the detailed reactive power at levels 1 and 3, that is, Qdet1 and Qdet3, are chosen for the classifier as inputs. These are calculated using the equation, Qdet =

1 2P

2 j 0 −1

∑d k =0

j 0,k

d “ j 0,k . 

FIGURE 10.2  Radial network for classification of power quality events.

Application of PNN and Wavelet Analysis to Classify Power Quality

221

10.4 WAVELET TRANSFORM-BASED FEATURE EXTRACTION 10.4.1 Capacitor Switching Figure 10.3 shows the capacitor-switching transients in the network of Figure 10.2. Capacitors of 50 to 550 µF are considered during the simulation of capacitorswitching transients. Various Qdet values from levels 1 to 5 at various buses are shown in Table 10.1 after switching on the capacitor at150 µF. Current and voltage signals

FIGURE 10.3  (a) Transient in current signal for capacitor switching, (b) signal decomposed at level 1, (c) signal decomposed at level 3.

TABLE 10.1 QdetValues for Capacitor Switching at Different Buses Qdet1

Qdet2

Qdet3

Qdet4

A

1.64 × 108

1.67 × 106

3.87 × 107

2.2 × 106

4.3 × 107

B

1.07 × 107

1.87 × 106

2.78 × 107

2.4 × 106

3.24.3 × 107

C

4.07 × 106

1.9 × 106

2.78 × 107

2.01 × 106

3.34.3 × 107

D

1.98 × 10

6

2.1 × 10

2.78 × 10

1.97 × 10

3.34.3 × 107

Y

1.05 × 10

6

2.44 × 10

2.78 × 10

2.3 × 10

BUS

6

7

6

7

Qdet5

6

6

3.34.3 × 107

Class 1 (C=150 µF Switching angle 60°)

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Microgrids

FIGURE 10.4  (a) Voltage transient during capacitor switching, (b) current during capacitor switching transient, (c) detailed reactive power at level 1, (d) detailed reactive power at level 3.

are taken at only the sending end. The voltage profile and current during capacitorswitching transients are shown in Figure 10.4(a) and (b), respectively. These voltage and current signals are decomposed up to level 5 utilizing DWT; then, according to j0 1 2 −1 the equation, Qdet = P ∑ d j 0,k d ” j 0,k . , harmonic reactive powers are calculated in 2 k =0 each decomposition level. In Figure 10.4 (c) and (d), the detailed harmonic reactive powers are shown at levels 1 and 3.

10.4.2 Motor Switching Figure 10.5 shows the motor-switching transients. To perform the simulation, induction motors with ratings 5 to 10 kW are taken, and the switching time is also changed for collection of a sufficient amount of data. Various Qdet values from levels 1 to 5 at various buses are shown in Table 10.2 after switching on the 9-kW induction motor at its peak voltage. In Figure 10.5(a) and (b), the voltage and current profile during motor switching transients are shown. In

Application of PNN and Wavelet Analysis to Classify Power Quality

223

FIGURE 10.5  (a) Voltage profile during motor switching transient, (b) current during motor switching transient, (c) detailed reactive power at level 1, (d) detailed reactive power profile at level 3.

TABLE 10.2 QdetValues for Motor Switching at Different Buses BUS

Qdet1

Qdet2

Qdet3

Qdet4

Qdet5

Class

A

2.1 × 108

6.1 × 108

2.9 × 107

5.5 × 108

2.4 × 107

B

6.1 × 107

4.21 × 108

6.76 × 106

3.75 × 108

6.2 × 106

C

4.6 × 107

3.3 × 108

4.05 × 106

2.56 × 108

3.67 × 106

D

3.3 × 10

2.4 × 10

3.45 × 10

1.87 × 10

2.78 × 106

2 (9 kw Switching angle 90°)

Y

2.7 × 107

9.7 × 107

2.56 × 106

9.1 × 107

1.97 × 106

7

8

6

8

Figure 10.5(c) and (d), detailed reactive power profiles at levels 1 and 3 are shown for motor-switching transients.

10.4.3 Fault-Induced Events Figure  10.6 shows the fault-induced transients and various Qdet values from levels 1 to 5 when asymmetrical faults occur at various buses, shown in Table  10.3. In Figure  10.6(a) and (b), the voltage and current during fault-induced transients are shown. In Figure 10.5(c) and (d), detailed reactive power at levels 1 and 3 are shown during fault-induced transients.

224

Microgrids

FIGURE 10.6  (a) Voltage transient during fault induced transient, (b) current profile during fault induced transient, (c) detailed reactive power profile at level 1, (d) detailed reactive power profile at level 3.

TABLE 10.3 QdetValues for Fault-Induced Transients at Different Buses BUS

Qdet1

Qdet2

Qdet3

Qdet4

Qdet5

A

2.5 × 105

1.2 × 106

2.3 × 106

1.1 × 106

6.8 × 105

1.2 × 10

7.2 × 10

4.2 × 10

3.2 × 10

5.67 × 105

4.2 × 106

2.1 × 105

2.1 × 105

Class

B

1.1 × 10

C

4.2 × 104

D

3.98 × 104

2.95 × 105

3.1 × 106

1.1 × 105

1.06 × 105

Y

2.44 × 10

1.48 × 10

2.1 × 10

6.7 × 10

2.08 × 104

5

6

4

6

5

6

5

4

5

3 (Rf = 20 Ω Switching angle 90°)

10.4.4 Converter Switching Transients produced through converters are shown in Figure 10.6, as well as 12 pulse converters with changing triggering angles. Various Qdet values from levels 1 to 5 at various buses are shown in Table 10.4. In Figure 10.7(a) and (b), the voltage and current profile during converter switching transients are shown, and in Figure 10.7(c) and (d), detailed reactive power profiles at levels 1 and 3 are shown during converter-switching transients.

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Application of PNN and Wavelet Analysis to Classify Power Quality

TABLE 10.4 QdetValues for Converter Switching at Different Buses BUS

Qdet1

A

5.21 × 10

4.9 × 10

1.68 × 10

4.49 × 10

5.45 × 10

B

3.61 × 103

4.77 × 104

1.68 × 106

4.51 × 104

4.56 × 107

C

1.53 × 103

4.58 × 104

1.68 × 106

4.74 × 104

3.45 × 107

D

1.25 × 10

4.55 × 10

1.68 × 10

4.77 × 10

2.34 × 107

Y

1.2 × 10

4.54 × 10

1.69 × 10

4.61 × 10

1.12 × 107

Qdet2

3

3

3

Qdet3 4

Qdet4 6

4 4

6 6

Class

Qdet5 4

4 4

7

4 (α = 60° firing angle)

FIGURE 10.7  (a) Voltage profile during converter switching, (b) current during converterswitching transient, (c) detailed reactive power at level 1, (d) detailed reactive power at level 3.

10.4.5 Transformer Switching Transformer-generated transients are shown in Figure 10.8. Transformers with various kilo-volt-ampere(KVA)ratings are selected to differentiate the inrush of transformer from other transients. Various Qdet values from levels 1 to 5 are shown in Table  10.5 after switching on the 100-kVA transformer. In Figure  10.8(a) and (b), the voltage and current profile during transformer-switching transients are shown. In Figure 10.8(c) and (d), detailed reactive power profiles at levels 1 and 3 are shown during transformer-switching transients.

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Microgrids

FIGURE 10.8  (a) Voltage transient during transformer switching, (b) current during transformer switching transient, (c) detailed reactive power at level 1, (d) detailed reactive power at level 3.

TABLE 10.5 QdetValues for Transformer Switching at Different Buses Qdet1

Qdet2

Qdet3

Qdet4

Qdet5

Class

A

1.05 × 105

5.98 × 105

1.83 × 107

6.07 × 107

6.2 × 105

B

1.04 × 105

5.97 × 105

1.78 × 107

5.99 × 107

6.01 × 105

C

8.9 × 10

5.85 × 10

1.78 × 10

5.2 × 10

5.9 × 105

D

1.09 × 105

5.2 × 105

1.79 × 107

3.45 × 107

5.28 × 105

5 (10 KVA Switching angle 60°)

Y

1.17 × 10

5.15 × 10

1.78 × 10

2.3 × 10

5.6 × 105

BUS

4

5

5

5

7

7

7

7

10.5 CATEGORIZATION OF TRANSIENTS USING PNN Simulation is carried out to classify the transients. Each type is considered as an individual class and assigned a classification number from 1 to 5. To calculate the DWT reactive power, MATLAB code is implemented, that is, Qdet1, Qdet2, Qdet3, Qdet4and Qdet5. Reactive power details are used as input to the PNN model. Seventy-five feature sets are obtained for each transient, from which 25 sets are utilized for training purposes such that 125 training samples are taken along with another 50 sets so 250 features are utilized for testing purposes. For every transient, to find testing and training data for the PNN model, the authors changed the capacitor capacitance

227

Application of PNN and Wavelet Analysis to Classify Power Quality

value, induction motor kW rating, converter firing angle, transformer kVA rating, fault resistance and fault occurrence time. Simulations gave 375 sets of detailed reactive power (Qdet) up to level 5 individually for the previous five transients. The specified groups of transient samples are given in Table  10.6. In conclusion, the rate of success for classifying the various types of transients also depends on the degree of decomposition. The success rate is characterized by adjusting the decomposition level in Table 10.7. This table indicates that the success rate remains the same whether the original signal is broken to level 5 rather than level 3. In the first, second, third and sixth rows, the success rate is also considered similar. However, the success rate is relatively low in the remaining rows. So the authors selected levels 1 and 3 to lower the computational load in view of all the findings. TABLE 10.6 Classes of Transient Samples and Simulated Data Set for Training and Testing of PNN Network Type of Transient

Assigned Class

Capacitor switching

1

25

50

Motor switching

2

25

50

Fault

3

25

50

Converter

4

25

50

Transformer switching

5

25

50

125

250

Total

Training Data (Set)

Testing Data (Set)

TABLE 10.7 Decomposition Level and Success Rate SL. No

Decomposition Level

Features Selected

Success Rate (%)

1

Decomposed up to level 5

Qdet1 to Qdet5

99.6

2

Decomposed up to level 4

Qdet1 to Qdet4

99.6

3

Decomposed up to level 3

Qdet1 to Qdet3

99.6

4

Decomposed up to level 2

Qdet1 and Qdet2

92.1

5

Decomposed at level 1

Qdet1only

21.4

6

Decomposed at levels 1 and 3

Qdet1 and Qdet3 only

99.6

7

Decomposed at levels 1 and 2

Qdet1 and Qdet2 only

97.6

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Microgrids

TABLE 10.8 Comparative Study with Other Existing Methods Sl. No.

Method for Identification of Power System Transients

Success Rate

1

Wavelet-transform+ANN based classifier[33]

95

2

Wavelet-transform based classifier [34]

95

3

S-transform +competitive neural network based classifier [35]

97.5

4

DTCWT+ ANN based classifier [6]

100

5

Proposed method

99.6

TABLE 10.9 Confusion Matrix Class

1

2

3

4

5

1

50

–

–

–

–

2

–

49

1

–

–

3

–

–

50

–

–

4

–

–

–

50

–

5

–

–

–

–

50

The results of the classification can be represented by a confusion matrix, which is standard for classifying studies. Table 10.8 indicates that there are one row and one column in an uncertainty matrix for every class. The row indicates the original class and the column reflects the PNN classification predicted class. In the matrix, the number displays the classification of different patterns observed from the test sample. For example, in Table 10.8, only 1 of 50 cases of motor switching was classed as faulty. For every transient, the error rate is defined in the table of the network classification. It can be concluded from these findings that the network properly classifies 249 of the 250 samples using this approach. This means that 99.6% is the correct classification rate. A plurality of grouping errors exists between classes 2 and 3 in Table 10.8, since they are identical in terms of characteristic harmonics.

10.6 COMPARATIVE ANALYSIS Table 10.9 demonstrates the comparative study with other existing methods. These methods are to some extent like the method proposed by the authors in the sense that the features of distorted signals were extracted using WT and then classified using ANN. The results presented in Table 10.9 show the proposed approach as well as better performance with a better success rate, which is 99.6%, along with lower computational complexity.

Application of PNN and Wavelet Analysis to Classify Power Quality

229

10.7 CONCLUSIONS In this chapter, an original, easy approach is proposed based on PNN and DWT for categorization of different switching transients. A precise and consistent power transient classifier is established to classify such transient signals. DWT was used to calculate the level 1 (Qdet1) and 3 (Qdet3) comprehensive reactive power utilized as the ANN model input. A probabilistic neural network is utilized as a classifier. Its reliability as well as the validity of numerous case studies is tested and indicates that this method is ideal for classifying the transients in power system networks.

REFERENCES 1. Sarıbulut, L. A  simple power factor calculation for electrical power system. Electric Power Energy System, 62, pp. 66–71, 2014. 2. Zwe-Lee, Gaing. Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery, 19(4), pp. 1560–1568, 2004. 3. Beg, M. A., Khedkar, M. K, Paraskar, S. R., and Dhole, G. M. Feed-forward artificial neural network–discrete wavelet transform approach to classify power system transients. Electric Power Components and Systems, 41, pp. 586–604, 2013. 4. Mao, P. L., and Agarwal, R. K. A novel approach to the classification of the transient phenomena in power transformers using combined wavelet transform and neural network. IEEE Transactions on Power Delivery,16, pp. 6654–660, 2001. 5. Chakraborty, S., Chatterjee, A., and Goswami, S. K. A  sparse representation based approach for recognition of power system transients. Engineering Applications of Artificial Intelligence, 30, pp. 137–144, 2014. 6. Chakraborty, S., Chatterjee, A., and Goswami, S. K. A dual tree complex wavelet transform based approach for recognition of power system transients. Expert Systems. http:// dx.doi.org/10.1111/exsy.12066. 7. Mokryani, G., Siano, P., and Piccolo, A. Detection of inrush current using S-transform and competitive neural network. In: Proceedings of the 12th International Conference on Optimization of Electrical and Electronic Equipment, 2010. 8. Ibrahim, W. R. A., and Morcos, M. Artificial intelligence and advanced mathematical tools for power quality applications: A survey. IEEE Transactions on Power Delivery, 17, pp. 668–673, April 2002. 9. Kucuktezan, C. F., and Genc, V. M. I. Dynamic security assessment of a power system based on probabilistic neural networks. In: Innovative Smart Grid Technologies Conference Europe (ISGT Europe) IEEE PES, Gothenburg, 11–13 October 2010.

11

Overview of Security and Protection Techniques for Microgrids Asik Rahaman Jamader, Puja Das, Biswaranjan Acharya, and Yu-Chen Hu

CONTENTS 11.1 Introduction................................................................................................. 232 11.2 Background.................................................................................................. 234 11.2.1 Key Characteristics of a Microgrid................................................ 234 11.2.2 Topology of Microgrids................................................................. 234 11.2.3 Types of Microgrids....................................................................... 235 11.2.4 DER Elements................................................................................ 237 11.2.5 Control of Microgrids.................................................................... 237 11.3 Structure of Microgrids............................................................................... 238 11.4 Problems in Microgrid Safety Systems....................................................... 239 11.4.1 Shifting Aspects in Error Importance...........................................240 11.4.2 Loss of Mains.................................................................................240 11.4.3 Avoidable Discontinuations...........................................................240 11.4.4 Blinding of Security.......................................................................240 11.4.5 Changes in the Short-Circuit Level................................................ 242 11.4.6 Prohibition of Programmed Reclosing........................................... 243 11.4.7 Unsynchronized Reclosing............................................................ 243 11.4.8 Wrong Stumbling........................................................................... 243 11.5 Different Methods of Protecting Microgrids............................................... 243 11.5.1 Distance Protection........................................................................244 11.5.2 Multi-Agent....................................................................................246 11.5.3 Current Regulator.......................................................................... 247 11.5.4 Variables Used in Protection..........................................................248 11.5.5 Adaptive Protection........................................................................248 11.5.6 Centralized Defense Scheme......................................................... 249 11.5.7 Decentralized Defense Scheme..................................................... 249 11.6 Discussion.................................................................................................... 250 11.7 Future of Microgrids.................................................................................... 250 11.8 Conclusion................................................................................................... 250 References............................................................................................................... 252 DOI: 10.1201/9781003121626-11231

232

Microgrids

11.1 INTRODUCTION Today, interest in electrical energy is persistently developing. To fulfill the growing need, more power plants must be developed. Normal energy or power plants have downsides, for example, carbon emissions, low proficiency, high transmission misfortunes, higher development and fuel cost, high development time and lower liability [1]. Consequently, the idea of a microgrid was developed, interfacing a few smallergenerators called miniaturized scale sources/distributed generators (500kW) overcharges turbines, sun-based force plants, wind energy units, smaller-scale turbines, small hydroelectric force plants, collective heat plus power as clusters, close to the low-voltage (LV) side to meet the necessary requirements. A microgrid is an energy-spreading complex network including distributed generation assets, various loads at the voltage level of appropriation and energy loading components. From a system point of view, a microgrid is beneficial in light of the fact that it is a controlled unit (CU), even though it can be misused as concurrenceevaluated capacity. From the perspective of clients, a microgrid may be intended to address uncommon necessities, for example, unwavering higher quality, better neighborhood voltages, expanded productivity, voltage hang rectification and unitperceptible influence flexibility. From an ecological point of view, a microgrid reduces natural contamination and the Earth-wide temperature boost since it creates less carbon monoxide (CO) [2, 3]. Regardless of how a microgrid is a fitting sub statute for constrained non-renewable energy sources and can proficiently take care of intensity age issues, it is still generally confined to a research facility scale because of various technical difficulties [4]. The most generally significant of these difficulties are protection, security, power excellence, activity in typical and islanded modes, voltage and recurrence regulators, attachment and play activity, vitality of the executive functions and framework solidity [5, 6, 7]. Table 10.1 lists a comparative analysis between the current grid and smart grids.

TABLE 11.1 Comparative Analysis between Current Grid and Smart Grids SL NO.

Current Grid System

New Intelligent Grid System

1

Electromechanical

Digital

2

Integrated Genesis

Distributed Genesis

3

Limited Sensors Device

AL Overall Sensors

4

One-Way Traffic

Two-Way Traffic

5

Hierarchical

Network

6

Blind

Self-Monitoring

7

Few Client Adoptions

Numerous Client Adoptions

8

Partial Control

Universal Control

9

Fails and Blackouts

Adaptive and Islanding

10

Physical Assessment

Distant Assessment

Security and Protection Techniques for Microgrids

233

An adoption framework that works as a microgrid brings an adequate age near the load and thus can keep up the force gracefully in the case of macrogrid issues. Likewise, if a microgrid has an overabundantage limit, it can give the macrogrid framework recovery assets, in this way shrinking the recurrence and span of blackouts. Such highlights add adaptability to the previously mentioned engineering and give microgrids a huge advantage for improving network flexibility to macrogrid failures. In any case, the advantages presented by a microgrid will be in danger if the microgrid isn’t appropriately ensured during the short-out errors that happen inside its own limits. In spite of the fact that microgrids are raised at the circulation level of the inheritance matrix, accessible delivery defensive equipment, for example, meld and reclose [4], are not an appropriate possibility for microgrid security because of different reasons, a significant part of which is very truthful and corporate defensive along with a variety of strategies have been distributed in the present form. The lower limit of the macrogrid can be bigger in some cases of the microgrid through more than one request for size, making the flaw current levels over the macro gridassociated and islanded methods of a microgrid pointedly extraordinary. Such a major dissimilarity makes organization of the current dissemination of defensive equipment troublesome and regularly out of reach. A variety of strategies have been distributed over the most recent couple of years to address microgrid promise issues [7, 8, 9]. In the interim, given the affectability of dependable assurance, broad testing and checks are required for a security plan to arrive at a business level. Even distributed exploration on microgrid protection has not approached this level yet. Without readymade microgrid transfer, defensive tools of sub-transmission and transmission structures, for example, directional overcurrent, departure and variance transmissions, are the main decisions that can make a protection framework progressively invulnerable to the difficulties of microgrids, for example, factor cut-off level, but at a greater expense. So, In spite of that there must be have some construct between all attribute of microgrid. These transfers are required to be the primary alternatives accessible to secure microgrids in any event for the following years. Figure 11.1 depicts the characteristic layer arrangement of the microgrid. These transfers are supported by many years of effective activity for microgrids; thus, the correct activity for microgrids is regularly underrated [10]. In any case, a key factor in the improvement of the vast majority of these transfers is that the age units incorporate straightforwardly coupled synchronous machines (SMs), a commonly unacceptable idea for the DER components of a microgrid. Accordingly, the impacts of DER parts, such as specific special qualities of microgrids on readymade defensive transfers, should be reviewed to recognize the fundamental services and adjustments to these transfers. These are fundamental steps to guarantee compact security of microgrids and thus the framework strength. This chapter is focused on the request of in-demand accessibility for microgrid security. Section 11.2 gives the background of this chapter. Section 11.3 discusses the structure of the microgrid. Section 11.4 explains challenges in microgrid protection systems, which consist of different challenges to protect microgrids. Section  11.5 contains a method to protect microgrids. Lastly, Sections 11.6 and 11.7 contain discussion and the conclusion.

234

Microgrids

FIGURE 11.1  A characteristic layer arrangement of the microgrid.

11.2 BACKGROUND This section surveys various classes and methods of microgrids and their segments. Figure 11.2 demonstrates the graphical chart of a microgrid that incorporates double equal feeders radiating from the point of common coupling on the microgrid.

11.2.1 Key Characteristics of a Microgrid • Connecting to the normal framework is optional • Flexible, steady quality and supportability are the center’s responsibilities. • Backup for all framework loads, not simply basic loads. Current innovations are expected to enhance vitality creation and use. DER units and loads inside the microgrid are equipped with nearby controllers. The level of improvement and functionalities of supervisory controller and energy management system (SC-EMS) can change broadly. As of now, there are no rules to smooth out such capacities, which can have huge consequences for the protection of microgrids.

11.2.2 Topology of Microgrids The microgrid structure can be circular, assembled or blended [11]. Topology is an element that influences the title and scale of issuing current and insurance methods in the microgrid. For example, a defective current separates in two equal ways but in a circular manner. Also, an upstream feeder detects double current on protection tools in every route inside a circle. On the other side, the current flow in the mess topology is equivalent for both upstream and downstream branches [12]. This chapter proposes protective measures for DC microgrids with circle topology.

Security and Protection Techniques for Microgrids

235

FIGURE 11.2  Diagram of a microgrid with connection.

The topology of the microgrid structure is shown in Figure 11.3. Its essential units include a circulation system, a bidirectional inverter, a vitality stockpiling part, a PV and fan power age and load, a sum of the five basic principles of the unit.

11.2.3 Types of Microgrids Considering the qualities/features of the feeders which form the foundation of a grid system, they are classified into three main points given in the following. Urban Microgrids: Here, feeders are located in compact mechanized areas or settled; these are heavily loaded, the primary trunk and side parts are adequately compacted and the grade of inequality is not high. The ratio of short-circuit in this case, with its PCC, is usually greater than 25. In this method, the voltage and fluctuation of a microgrid during microgrid-related processes is controlled by the grid system, and the profile of voltage is actually smooth. In this way, in some cases, synchronization is noticed by the electronically coupled distributed generation (ECDG) part in the microgrid, which is important for reliability. Rural Microgrid: Insufficiently populated areas are the location of feeders in this class, so the load is dismantled. The short-circuit ratio is not really higher than that of the urban microgrid, and the profile of the voltage is not smooth. In this microgrid, abnormalities of voltages and changes can be noticeable. Therefore, DER elements

236

Microgrids

FIGURE 11.3  Microgrid topology.

have impact the power (volts), and whenever allowed, the feeder voltage can be controlled per guidelines. Off-Grid Microgrids: Off-network microgrids are located in geographically remote areas where there is no scope for microgrid links or which are bounded by problematic scenarios for broadcast route associations. To be specific, the off-grid microgrid functions continuously in islanded mode and so does not conform to the exact meaning of “microgrid.” It actually does not obey the rules of microgrid-associated activity. The incorporation of large DER elements with off-grid microgrids is happening faster than urban and provincial microgrids, as is work on SC-EMS procedures for off-network microgrids. There is serious social, financial, political and natural impulses and tension concerning the coordination of sustainable power sources with urban and country microgrids and off-grid microgrids, which are being built entirely for islanded mode. Thus, this type of microgrid will primarily be the fastest growing in the microgrid family, considering all the difficulties in the microgrid family. Most contracted scale networks can be one of five classes (as characterized by the Microgrid Institute): • Off-grid microgrids  that are islanded and have remote destinations and small-scale medium frame brushes are not associated with neighboring utility systems.

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• Campus microgrids that are fully connected to a neighborhood utility mesh but can keep some degree of administration in isolation from the network, for example, during a utility blackout. Mill models run college and corporate grounds and containment facilities and serve military installations. • Community microgrids that are involved in utility systems. Such microgrids serve different clients or administrations within a network and provide robust capacity to largely unavoidable network resources. • District energy microgrids that give power just as heat strength for warming (and cooling) several offices. • Nanogrids incorporate the smallest isolated structure units with the skill to work liberally. A Nanopattern is characterized as an introverted configuration or a private strength gap.

11.2.4 DER Elements DER elements are electronically paired and divided into spinning machine units. The main purpose of this classification is that the interface medium shows as much reliability and control as the conventional qualities of a unit. ECDG Units: In this type, an alternative or direct current, a voltage sourced generator, mediates between the microgrid and origin [11, 12]. A voltage-sourced converter is divided into either a single stage or three stages depending on the method of energy creation. Type-4 wind turbine frameworks, voltage-sourced converter PV frameworks, class-4 wind turbine frameworks and medium-sized rapid gas-turbinemachine frameworks use distributed generation techniques. This can be non-dispatch able or dispatch able units. MPPT controls the supply for a non-dispatch able unit [13]. Pivoting Machine-Coupled Distributed Generation Sections: Spinning machines are adopted and incorporated by this section with the following points: • Class-1 SCIM utilized by WTG • Class-3 DFIM merged with WTG; diesel-producer elements, which receive recorded, controlled SM

11.2.5 Control of Microgrids The control of microgrids includes three types of method: centralized, scattered and progressive controls [14]. Certain unmistakable properties of the microgrid, for example, the maximum level of irregularity and the mixed variety of DER elements, emphasize the difficulties in formulating an appropriate control for each working condition. In addition, two special task modes (i.e., islanded and connected modes), plus the versatile nature of microgrid control, can be further enhanced as needed. Meanwhile, the regulation of automatically coupled elements has a great influence on the grid’s temporary conduct, especially due to shortcomings, which inevitably disturb the protective parts of the grid system. A basic step taken to handle microgrid regulation/ security issues is to distinguish different methods of activity, as described in the following.

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Macrogrid-Connected Approach: In this mode, every DER element incorporates a load controller that relies on the component’s privately predicted current and voltage and their consequences. SC-EMS of microgrids produce DER orientation waveforms that are expected to organize the activity of DER elements. In this mode, SC-EMS is similarly a choice to obtain data from the macrogrid. Islanded Approach: Here, in operation, for internal activity only, LC and SC-EMS are used in the microgrid. The regulator methods of microgrid-related islands and macrogrid-related systems are often remarkable, as in macrogrid-related modes, all DER elements can function in PQ-switch method. Control progress should be addressed and monitored through SC-EMS. Mesogrid Approach: A mesogrid is characterized as a bunch of microgrids, each with an SC-EMSs and LSs, which are associated with a host macrogrid and work in a pre-specified facilitated way. Each microgrid inside the mesogrid works as a simulated force unit dependent on certain orders that are created through its SC-EMS and imparted to LCs inside the microgrid. The upper layer of the SC-EMS, the mesogrid SC-EMS, gets data from the macrogrid and from every individual microgrid and, on that premise, determines the control/activity duties of each microgrid concerning its PCC on this basis.

11.3 STRUCTURE OF MICROGRIDS A microgrid comprises three important parts: miniaturized scale generators (according to the paradigm, a PV cluster and power module producer), neighborhood storage components and various loads. A microgrid can be a private or three-stage framework; it might be associated with minimal or mid-voltage dissemination organization and be able to work in regular and islanded modes [15]. A graphical portrayal of an example microgrid is shown in Figure 11.4 [16]. Figure 11.4 defines the DC, AC, and high-frequency AC atmosphere. ADC microgrid is a movement structure linking DC loads, energy storage components and distributed generation assets that are normally bottomless, with DC voltage as outcome. On the other hand, an AC microgrid comprises AC transports, is suitable for mass association and loads to its belongings. These transports are used as bidirectional converters with the help of the DC method. Figure 11.4 defines the zonal position as 1 for DC and zones 2 and 3 for AC microgrids. DC microgrids utilize a convertor in both DC and AC classifications and as a crossing point from DC to AC microgrids [17]. The associated distributed sustainable power source belonging to DC transports does not moderate the universal production of the organism, because a reduction in the extent of converter rub-down lowers the requirement of vitality altering, but it expands the strained nature of the arrangement. High-frequency alternative current (HFAC) frameworks are now used in aircraft and train technology. The use of frequencies from 400 Hz to 20 kHz for power allocation frameworks is currently under investigation. The preferred position of HFAC frameworks is that the size is very small, with extraordinary power depth, and private. However, the application of these frameworks to microgrids is uncertain because maximum repetition can result in maximum power misalignment and a voltage drop with the current.

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FIGURE 11.4  Sample of AC and DC microgrid.

11.4 PROBLEMS IN MICROGRID SAFETY SYSTEMS To get better protection, a selection will be made for strong security activities, theoretical activities, stabilization, adaptation, legalization of rational equipment related to the best rankings and less testing. As surveyed before, conventional protection plans are intended for redial streams with high shortcomings. In reference to the previous discussion, the conventional protection schemes outlined for radial flow with high fault currents for distribution networks will not function without error in a microgrid because of bilateral power flow, evolving characteristics of distributed generations, sporadic nature of distributed generations and variation in fault current [18]. The crucial challenges faced in the protection of microgrids are:

1. Shifting aspects in error importance 2. Loss of mains 3. Avoidable discontinuations 4. Blinding of protection 5. Changes in the short-circuit level

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6. Exclusion of routine reclosing 7. Unsynchronized reclosing 8. Prohibition of automatic reclosing

11.4.1 Shifting Aspects in Error Importance The distributed generation connection in LV has been modified to a significant level of the main level because of privacy and islanded modes. In grid-combined mode, the event is much higher because both the internal use of the microgrid and the distributed generation signal are available. In any case, there is a liability in the slow speed of time, and the boundaries of the original source in the microgrid are determined. The current fluctuation depends on what distributed generation type is used [19]. Additionally, new natural source-based distributed generations exist as exceptionally long-term distributed generations, as a result these distributed generations are simply imputing the current situation. Therefore, the error magnitude of the current depends on the method of operation. As a result, it is difficult to accurately estimate the current state of the error.

11.4.2 Loss of Mains Loss of mains (LOM) defines the discontinuation utility of microgrid activation from the origin, although it is associated with the part of the utilized gate. (1) Error of utility grid and (2) issue in circuit roller associating with the main utility. During such unplanned islanding, since a piece of the system is charged and the islanding sun identified, there is a danger of error [18]. Additionally, this procedure brings about uncontrolled voltage and recurrence, dangers to plants accessible in the islanded framework and unsynchronized terminations which eventually bring in the destruction of client apparatus and computing devices.

11.4.3 Avoidable Discontinuations This is significant when a distributed generation is found near a substation and in any occasion when there is a crisis near the substation of the neighboring feeder (Figure 11.5); at that point, in such a case, significant portion of the deficient current is contributed by the distributed generation. This makes the current surpass its pickup estimation accessible in the solid feeder (Relay1), and subsequently it stumbles before the hand-off compared to the faulty feeder (Relay2).

11.4.4 Blinding of Security Both the utility and the distributed generation connect the critical current when there is a problem with the bottom completion of the feeder in a microgrid. Currently, Thevenin’s barrier given at the error point has been extended in contrast to the foundation method due to the extra barrier given through the distributed generation.

Security and Protection Techniques for Microgrids

FIGURE 11.5

241

Avoidable discontinuations.

In Figure 11.6, a small distributed generation is added some way off. For a lack located in a well far from L, each stage is determined as the most extreme issue. Ik =

Votth

(11.1)

3Zth

whereVotth is the voltage fault at the faulty point, and Z is Thevenin’s low impedth ance. Let z , z , z be different states defining origin of utility, impedance of distributed o g L generation and transmission line, respectively. At that point, Thevenin’s proportional circuit of the system can be shown as Figure 11.7. Thevenin’s low is determined as Z th =

(Z

(Z

s

s

+ l .Z L ) .Z g

+ l .Z L + Z g )

+ (1 − l ) Z L

(11.2)

Therefore, Thevenin’s impedance offered at the blamed point is expanded because of the extra impedance offered by distributed generation. The current contributed from the lattice is I grid =

(Z

Zg

s

+ l .Z L + Z g )

.I k

(11.3)

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Microgrids

FIGURE 11.6  Blinding of assurance.

FIGURE 11.7  Thevenin’s equivalent circuit.

The promise of the current deficit by the usefulness is not linear with the field and size of the distributed generation. Later, when an error arises at the bottom of the feeder in a microgrid, the lattice barrier will be as wide as the distributed generation barrier, and so the short-circuit current below the feeder hand-off surprise (pick) current in the LV is good, which causes the connection to identify the problem and thus the entire insurance structure. So in order to overcome the issues described previously, it is necessary to create a valid insurance system to withstand the problems described previously and to work efficiently. As a result, the region audits various security plans accessible to the microgrid.

11.4.5 Changes in the Short-Circuit Level The ability to operate the microgrid in protected and islanded mode is an important test for the appropriate security plan. This problem causes significant differences between constraints in these modes. In normal operation mode, a microgrid is connected to a medium voltage (MV) array and, to provide the resulting flow, both the system and the distributed generation are connected. Due to the need for circuit breaker (CB) replacement, the commitment of the distributed generations may delay the system above its approved respect. In islanded group mode, the microgrid

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separates from the MV system. Thus, in small events, only the distributed generation lacks flexibility. Since the estimate of the problem provided by the director general is limited [21], the current level of error in this landslide mode is fundamentally lower than in normal mode. This level may be peculiar to traditional insurance structures, which are designed based on the size of the current deficit and may not be able to identify problems. This problem requires a strategy that can ensure microgrids in both normal and geo-mode.

11.4.6 Prohibition of Programmed Reclosing The scattered system spreads without the distributed generation. Then, when restoration work is done, the flowing part of it is detached to clear transient errors. In the presence of the distributed generation, both the MV system and the distributed generation are strongly deficient. However, the recovery separates the MV system, the distributed generation handles the current deficit until the error is recovered and, in this situation, the transient problem turns into a permanent issue.

11.4.7 Unsynchronized Reclosing At this point, when a distributed generation is connected to a system, two unknown structures are closely linked. If the association is run without thinking about synchronization, it can damage the micro-hardware by extending the distributed generation. It is important to note that the “single wire earth return” promotions are seen as nothing but “waiting issues” that empower easily accessible customers, only through direct access or receiving via earth [22].

11.4.8  Wrong Stumbling Bogus stumbling happens when the shortcoming current happening in a feeder is enhanced by the flawed current produced through a distributed generation in a neighboring feeder appended to a similar sub-station. In this situation, the defensive hardware of the neighboring feeder may disengage the circuit, producing an issue called pointless blackout of feeder or bogus stumbling [23].

11.5 DIFFERENT METHODS OF PROTECTING MICROGRIDS A valid security plot for the microgrid is to be structured so that in case of any deficiency, a base section of the framework is separated without affecting the rest of the framework. This should be possible with a mix of essential and back-up defensive equipment. The defensive equipment required to identify any defect inside its field. In order to work, reinforcement at that point gets into equipment activity. The simple framework is spiral, and a subsequent security plot is planned with current transfers and wires that are very basic. As discussed before, with the introduction of distributed generations in the current framework, the structure of force becomes progressively more complex. Additionally, the flawed stream is unique because it varies with the method of activity, the number of the director-general and the

244

FIGURE 11.8

Microgrids

Organization followed for microgrid protection.

director-general. Subsequently, planning a productive insurance plan to adapt to the previously mentioned issues is a difficult task. To organize different insurance plans (Figure 11.8), an equivalent has been discussed.

11.5.1 distance pRotection Remote protection that offers high selectivity applies significantly to transmission lines. The impedance equation is used to determine the train hand-off point Zm =

Votm im

(11.4)

where Votm is the calculated voltage and the calculated current at the transfer opinion. Under normal circumstances, intentional impedance incorporates heap impedance,

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and subsequently the value is higher. In any case, in the event that there is a deficiency in the line, the intentional impedance in such cases at that point would be same as the route impedance, which is extremely small. Thus, to determine intentional impedance and value, the occurrence of defects and what happened, deficiencies within the specific insurance sector are recognized. Generally, three areas of protection are advertised for a district. Zone 1 insurance is transient protection covering 80% of the area. Zone 2 insurance covers the area revealed through region 1. Region 3 provides insurance for a nearby link. Both zones 2 and 3 work in clear time, and the time of activity of zone 3 is more noteworthy than that of zone 2. This priority is picked so that Z , Z ,Z The set S1 S2 S3 worth is determined as Z S 1 = kr 1 ×Zline

(11.5)

Z 21 = kr 2 × (Zline + Z S 1 )

(11.6)

Z S 3 = kr 3 × (Zline + Z S 2 )

(11.7)

where k , k ,k are the risk coefficients of each zone, for the most part, set as an r1 r 2 r3 incentive between 0.8 and 0.85. For distance reduction, a few steps applied with the microgrid (Figure 11.9) additionally add current to the distributed generation associated with bus 3. Directorgeneral impedance will similarly generate line impedance, with the impedance seen from line 23 having high contrast with the impedance on bus 2. The impedance estimated by transfer ℜ is 23

Zm =

Votm im

=

i1Zline 23 + i3Z κ i1

(11.8)

The intentional impedance set is transmitted by an incentive according to Eq. (8), and insurance selectivity is needed. Therefore, the estimated impedance in the upstream/ downstream system is disturbed by the idea of distributed generation with a wide

FIGURE 11.9  Distance protection.

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Microgrids

range. To decrease the effect of the in feed current by distributed generation in the insurance, the set estimation of the transfers is recalculated as Z S 1 = κr 1 × Zline 

(11.9)

Z 21 = κr 2 × (Zline + κβ Z S 1 ) 

(11.10)

Z S 3 = κr 3 × (Zline + κβ Z S 2 ) 

(11.11)

The new transfer settings are associated with great selectivity and efficacy and make assurance plans to work successfully for islanded mode. Elements in the hand-off setting are used to ensure segregation with the aid of a focal controller due to occasional changes occurring in the system dependent on the position of the distributed generation and circuit breaker. The setting for each conceivable design (contingent on the state of the distributed generation and CB) is disconnected and put into an activity table.

11.5.2 Multi-Agent This type of agent is a gathering of smart equipment and programming operators distributed in the system and works in combination to accomplish an overall objective, the effective security of the grid system. The engineering for a multi-specialist plot intended for versatile over-current assurance includes three layers: hardware, substation and framework. These layers are defined in Figure 11.9. A high range of current transfers is located in different pieces of the microgrid. The gear level is the most reduced level in a multi-expert organization and includes speculation operators, defender specialists, multipurpose specialists and entertaining specialists. The measurement consultant screens the surrounding parameters (1 and 5) and takes them to the defender specialist. The defender operator checks to see if there is a problem with the nearby factors. At the time the event recognizes the subject, it sends data to the entertainment operator, and the entertainment operator trips the appropriate circuit breakers to disassemble the broken part. The data of the multipurpose operator hardware works at the substation level. The substation level speaks through the gear level and moves to the data framework level. The substation level includes district specialists and executive operators. Region operators transmit the acquired data from portable experts to structure-level and board experts. The administration specialist determines the settings of the transitions to join the structure changes in the grid system and then hands it over to the defender operator. The most noticeable layer shapes the framework layer. The framework layer enters the regulator to organize the microgrid, speaks to the substation level and screens the entire system. It refreshes itself with system data that includes nearby factors, for example, the status of CBs, the status of distributed generations, electricity and power. In terms of these factors, it drives the mass flow and determines it theoretically. Different types of parameters are sets for transfers and discusses it with substation level. The framework level includes a framework specialist and evaluation operator. The data obtained from the district expert is sorted by the framework operator,

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FIGURE 11.10  Multi-agent system.

and only the basic information is sent to the assessment operator. The evaluation expert reviews the assurance structure. In this event, it identifies any required insurance, instantly sending an adjusted setting to the administrative expert to accomplish smooth, multifaceted protection with the help of a multi-operator plot.

11.5.3 Current Regulator Using the structure of a microgrid, fault current limitation is set up for the PCC to minimize the shortfall current. Fault current limiters (FCLs) are preserved at a

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Microgrids

minimum in normal conditions, and in most extreme situations, they are set below fault conditions [22]. Insufficient current limiting filters will be extensively sorted into three types: strong condition FCL, excellent indicator FCL and electromagnetic FCL. In the first, the electricity-minimizing behavior depends on the nonlinear response of the electric conducting materials to the temperature, electricity and area of interest [24]. Under normal conditions, the current stays below the edge; as a result, the whole current is given to defensive equipment. The currently restricted behavior in the powerful-state FCL is chosen depending on the on/off condition of the conductor equipment. In the last type, current-minimizing behavior is chosen depending on the variety of attractive fields. The top deficit in the top half of the half-species is the current regulator [25], which is a mixture of superconducting FCL, strong-state FCL and electromagnetic FCL. For limiting current, this includes one of the proposed approaches in the microgrid system.

11.5.4 Variables Used in Protection 1. Microgrid security should be dependent on numerous factors, for example, current, voltage, edges, voicing waves, wavelet packet transformation (WPT) and THD tests that should be deliberated in detail, as follows. 2. Node powers. For the state factor, node voltages are used. The build-up between the intentional voltage and the state-rated voltage is extraordinary, but it indicates the proximity of the error; if not, the line is considered strong. State factors for the under-region are assessed. So the amount of correspondence required in this protection plot is lower. 3. WPT. Wavelet packet change is used to isolated, circulate and also limit all repetitive substances in the handled mark. It gives the signal in time and repeats limited signals which measure the repetition substance of the handled signal. When power electronic regulators are used, the three-phase sum is transformed to the edge of the DQ, and later the WPT is used for the signal. Measures of information of minimum detail length (MDL) are used to select the work of the ideal wavelet base and show that the Douchchies db4 waveform acts as the ideal. 4. Traveling wave. Traveling waves are separated by a mathematical morphological filter (MMF) that forms a clear element of magnification and disruptive signals. The two waves are separated and their time and intensity are kept separate. In the event that there is a fault in the first half, that point result of the poles of the two waves has a positive value, and in the case that it is contained in the second half, it will be negative.

11.5.5 Adaptive Protection Adaptive protection is “an online movement that changes the preferred defensive response to adjustment in the position of the framework.” A general plan for a versatile security framework has been introduced. As indicated by this arrangement, the

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assurance framework constantly screens the framework and implements new insurance coordination if any adjustment in topology or activity should occur. Prerequisites are required for this system, 1. Using directional and DOC transfers. 2. DOC must have cluster settings on the current transfer. These aggregated settings are changed locally or remotely. 3. Using a fast and secure informational foundation in a system of open conferences, such as IEC61850. Adaptive protection can be made real and larger by a centralized and decentralized structure or two structures of multi-operators. In the two structures, the defensive sets of the microgrid should be refreshed in the event of any adjustment in the system. This is on the web or disconnected. In the disconnected arrangement, each setting is simply cut into the memory of the experts or supervisory remote control unit (SRCU) for a dissimilar arrangement of the microgrid, and it is given a mode. SRCUs or specialist examinations use neighborhood information or take examples through an open framework. This activity is done online after each adjustment in the system. Data related to organized change help SRCUs or operators use related settings and use them as outing settings as long as the status does not exist. The creators of [26, 27] tested this technique for two reasons. First, they claim that this strategy cannot cover all situations due to the various unique situations that exist in microgrids. They argue that this multifaceted strategy cannot evaluate synchronous problems because it requires a large amount of information augmentation due to the high number of possible situations to be measured.

11.5.6 Centralized Defense Scheme This configuration uses an SRCU that is suitable for communication with transmission substation equipment through a fast and secure open infrastructure and transmits the necessary control and protective requests to equipment that monitors the situation and system conditions in general. The system should be provided to the SRCU as input, including the number and name, the load measurement existing in each transport and the properties of the force switches. According to the arrangement introduced in [28], this problem is due to the constant change of microgrids. Despite this investigation, use online considerations for insurance adjustment [29]. In these investigations, if a distributed generation cooperates with or separates from the system, a regulator signal is directed to the SRCU to make new assumptions on the web and sets the electricity of each transfer activity according to the present condition [30].

11.5.7 Decentralized Defense Scheme In opposition to focal versatile insurance, decentralized versatile assurance or multioperators have advanced speed, consistent quality and expansibility, contrasting with

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Microgrids

brought-together security. Decentralized versatile assurance consists of many disseminated specialists in various equipment in the system that work in the whole organization, and can communicate through an informative framework and are consistent with neighborhood and wide-zone information [31]. For the most part, a specialist framework incorporates:

1. Autonomy: a specialist examines work conditionally and without the impedance of humans or different operators, per nearby circumstances. 2. Reactivity: specialist filters find modifications in input information and respond according to them. 3. Pre-liveliness: the specialist response includes information in a manner that fulfills the reasons for their structure.

11.6 DISCUSSION The important parts exist in microgrid and for which, protection required are thosedistributed generation, line, load and PCC. The modifier presence in the PCC should be planned with different security transfers and PCC current over, under-voltage, over-voltage, under-and over-repetitive transfers. The most suitable protection for microgrid is primary security with extra current as back up. However, by evaluating the various security mechanism aids designed for microgrids, it is recommended that multidisciplinary insurance is the best insurance. As a result, the versatile balance and scattering lines of different insurance frames are the best insurance scheme compared to the current protection for microgrid loads. The cost to implement versatile protection plots with advanced transfers and sensors on the raw edge is extremely high, but this cost is matched with required energy costs and lastly if this opportunity considers for long duration, then it provides better result.

11.7 FUTURE OF MICROGRIDS Microgrids are important to the world and the future. We should examine some points demonstrated in Figure 11.11. • They expand the effectiveness of bigger power grids. • They offer force flexibility against catastrophic events, for example, seismic tremors, waves and storms. • They help focus catastrophe aid during provincial and national emergencies. • They are secure against digital and physical assaults. • They are beneficial for the earth since they use renewable assets. • They create job and training openings.

11.8 CONCLUSION Today’s world demand high-end development with various application of distributed technology. Higher requirement for distribution technology needs higher protection. This chapter dissects the techniques proposed in various investigations to take care of

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FIGURE 11.11  An illustration of the future of microgrids.

the issue of insurance in a microgrid. In this chapter, some standard assurance plans are investigated, which are applicable in a dissemination framework with smallerscale matrices. Because of the preparation of a strong framework and the innovations of wide region estimation and PC security, specialists have begun to use new strategies to structure insurance control arrangements of networks. It was found that the existence of distributed generation units and microgrid capability of operation in two normal and islanding modes may change fault current level while causing false tripping, blindness of protection, prohibition of automatic reclosing and unsynchronized reclosing. In such a manner, key variables for structuring a suitable assurance framework for microgrids are talked about thoroughly. These comprise microgrid type and topology, distributed generation type, message interface type and interval time, a technique for shortcoming identification and investigation, transfer type, issue type, strategy for establishing and utilization of transformers in the microgrid. Thinking about these elements, the different strategies projected so far for microgrid security were discussed. The survey underlines that utilizing a disconnected choice strategy in microgrid security can sabotage a vigorous assurance plan. This is because of the way that, with disconnected choices, all imaginable microgrid activity in all topologies may not be remembered for the insurance plan. On the other hand, utilizing a multi-specialist strategy is seen as the main technique equipped for reacting to microgrid auxiliary changes with high adaptability and consistent quality. In this technique, one can utilize suitable correspondence connections to investigate all system changes on the web and choose the best defensive procedure as indicated by the

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Microgrids

microgrid topology. Although, the vulnerabilities in correspondence joins and defensive hardware are seen as a challenge for this technique. A multipurpose insurance framework, applying new advancements, for example, PMU and GPS, a strong assurance framework and so on will be the future course of properly arranged security.

REFERENCES 1. Basak, P., et al., “A Literature Review on Integration of Distribute Energy Resources in the Perspective of Control, Protection and Stability of Microgrid,” Renewable and Sustainable Energy Reviews, 16 (2012): 5545–5556. 2. Chen, J., et  al., “The Overview of Protection Schemes for Distribution Systems Containing Micro-Grid,” Proceedings of Power and Energy Engineering Conference, 2011, pp. 1–4. 3. Conti, S., “Protection Issues and State of the Art for Microgrids with Inverter Interfaced Distributed Generators,” Proceedingsof International Conference on Clean Electrical Power (ICCEP), 2011, pp. 643–647. 4. Shanglin, Zhao, Wu Zaijun, Hu Minqiang, et  al., “Thoughts on the Protection of Distributed Generation and Piconet,” Power System Automation, 34.1 (2010): 73–77. 5. Che, L., et al., “Adaptive Protection System for Microgrids: Protection Practices of a Functional Microgrid System,” IEEE Electromagnetic, 2 (2014): 66–80. 6. Shahriari, S. A. A., M. Abapour, A. Yazdian, et  al., “Minimizing the Impact of Distributed Generation on Distribution Protection System by Solid State Fault Current Limiter,” Transmission and Distribution Conference and Exposition, 2010 IEEE PES, 2010,pp. 1–7. 7. Nikkhajoei, Hassan, and Robert H. Lasseter, “Microgrid Protection,” 2007 IEEE Power Engineering Society General Meeting, IEEE, 2007. 8. Hooshyar, Ali, and Reza Iravani, “Microgrid Protection,” Proceedings of the IEEE, 105.7 (2017): 1332–1353. 9. Brearley, Belwin J., and R. Raja Prabu, “A  Review on Issues and Approaches for Microgrid Protection,” Renewable and Sustainable Energy Reviews, 67 (2017): 988–997. 10. Shiles, J., et al., “Microgrid Protection: An Overview of Protection Strategies in North American Microgrid Projects,” 2017 IEEE Power & Energy Society General Meeting, IEEE, 2017. 11. Gururani, Ashika, Soumya R. Mohanty, et al., “Microgrid Protection Using Hilbert– Huang Transform Based-Differential Scheme,” IET Generation, Transmission  & Distribution, 10.15 (2016): 3707–3716. 12. Khandare, Pooja, S. A. Deokar, and A. M. Dixit, “Advanced Technique in Micro Grid Protection for Various Fault by Using Numerical Relay,” 2017 2nd International Conference for Convergence in Technology (I2CT), IEEE, 2017. 13. Nuñez-Mata, Oscar, et  al., “Development of a Microgrid Protection Laboratory Experiment for the Study of Overcurrent and Undervoltage Functions,” 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), IEEE, 2017. 14. Habib, Hany F., Christopher R. Lashway, et al., “A Review of Communication Failure Impacts on Adaptive Microgrid Protection Schemes and the Use of Energy Storage as a Contingency,” IEEE Transactions on Industry Applications, 54.2 (2017): 1194–1207. 15. Kar, Susmita, S. R. Samantaray, and M. Dadash Zadeh, “Data-Mining Model Based Intelligent Differential Microgrid Protection Scheme,” IEEE Systems Journal, 11.2 (2015): 1161–1169. 16. Memon, Aushiq Ali, and Kimmo Kauhaniemi, “A  Critical Review of AC Microgrid Protection Issues and Available Solutions,” Electric Power Systems Research, 129 (2015): 23–31.

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17. Cuzner, Robert M., and GiriVenkataramanan, “The Status of DC Micro-Grid Protection,” 2008 IEEE Industry Applications Society Annual Meeting, IEEE, 2008. 18. Laaksonen, HannuJaakko, “Protection Principles for Future Microgrids,” IEEE Transactions on Power Electronics, 25.12 (2010): 2910–2918. 19. Salomonsson, Daniel, Lennart Soder, and Ambra Sannino, “Protection of Low-Voltage DC Microgrids,” IEEE Transactions on Power Delivery, 24.3 (2009): 1045–1053. 20. Ustun, TahaSelim, CagilOzansoy, and AladinZayegh, “Modeling of a Centralized Microgrid Protection System and Distributed Energy Resources According to IEC 61850–7–420,” IEEE Transactions on Power Systems, 27.3 (2012): 1560–1567. 22. Kar, Susmita, and Subhransu Rajan Samantaray, “Time-Frequency Transform-Based Differential Scheme for Microgrid Protection,” IET Generation, Transmission  & Distribution, 8.2 (2014): 310–320. 23. Ustun, Taha Selim, Cagil Ozansoy, et al., “A Microgrid Protection System with Central Protection Unit and Extensive Communication,” 2011 10th International Conference on Environment and Electrical Engineering, IEEE, 2011. 24. Oudalov, Alexandre, and Antonio Fidigatti, “Adaptive Network Protection in Microgrids,” International Journal of Distributed Energy Resources, 5.3 (2009): 201–226. 25. Olivares, D. E., et  al., “Trends in Microgrid Control,” IEEE Trans. Smart Grid, 5.4 (July 2014): 1905–1919. 26. Al Lee, G., and W. Tschudi, “Edison Redux: 380 Vdc Brings Reliability and Efficiency to Sustainable Data Centers,” IEEE Power & Energy Magazine, 10.6 (November 2012): 50–59. 27. Interconnection for Wind Energy, United States of America Federal Energy Regulatory Commission, Docket No. RM05–4–001, Order No. 661-A, December 2005. Available: www.ferc.gov/EventCalendar/ Files/20051212171744-RM05-4-001.pdf 28. Zeineldin, H. H., Y. A. R. I. Mohamed, V. Khadkikar, and V. R. Pandi, “A Protection Coordination Index for Evaluating Distributed Generation Impacts on Protection for Meshed Distribution Systems,” IEEE Trans. Smart Grid, 4.3 (September  2013): 1523–1532. 29. Line Distance Protection REL670 Application Manual, Document 1MRK 506 315-UENABB, Vasteras, Sweden, December  2012. Available: www.05.abb.com/ global/scot/ scot387.nsf/veritydisplay/1d6bacf573307830 c1257b0c0046292a/$file/1 MRK506315-UEN_C_en_Application_manual__REL670_1.2.pdf 30. Ziegler, G., Numerical Distance Protection: Principles and Applications, 4th ed. Erlangen, Germany: Publicis, 2011, ch. 6. 31. Ustun, Taha Selim, and Reduan H. Khan, “Multiterminal Hybrid Protection of Microgrids Over Wireless Communications Network,”  IEEE Transactions on Smart Grid, 6.5 (2015): 2493–2500.

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Multilevel VoltageBased Coordinating Controller Modeling, Development, and Performance Analysis Ranjit Singh Sarban Singh, Tiara Natasya Abdul Halim, T. Joseph Sahaya Anand, and Maysam Abbod

CONTENTS 12.1 Introduction and Review of Chapter............................................................ 255 12.2 System Integration: Multilevel Voltage-Based Coordinating Controller.... 256 12.2.1 Electronic Stateflow Chart Circuit................................................. 257 12.2.2 Electronic Logic AND Gate Circuit.............................................. 259 12.2.3 Electronic Conditional Switching Circuit...................................... 259 12.2.4 Current Conversion Circuit............................................................ 259 12.3 Results and Discussion................................................................................ 262 12.3.1 Solar and Wind State flow Chart Outputs...................................... 262 12.3.2 Condition Two: 12 V ≥ Vsolar ≤ 15 V and 10 V ≥ Vwind < 12 V........ 263 12.3.3 Condition Three: 10 V ≤ Vsolar < 12 V and 12 V ≤ Vwind ≤ 15 V......266 12.3.4 Condition Four: 0 V < Vsolar